diff --git a/data/ref_samples_area/FAOSTAT_data_en_3-13-2024.csv b/data/ref_samples_area/FAOSTAT_data_en_3-13-2024.csv
new file mode 100644
index 00000000..8ad66929
--- /dev/null
+++ b/data/ref_samples_area/FAOSTAT_data_en_3-13-2024.csv
@@ -0,0 +1,97 @@
+Domain Code,Domain,Area Code (M49),Area,Element Code,Element,Item Code,Item,Year Code,Year,Unit,Value,Flag,Flag Description,Note
+"RL","Land Use","108","Burundi","5110","Area","6620","Cropland","2018","2018","1000 ha","1539","I","Imputed value",""
+"RL","Land Use","108","Burundi","5110","Area","6620","Cropland","2019","2019","1000 ha","1566","I","Imputed value",""
+"RL","Land Use","108","Burundi","5110","Area","6620","Cropland","2020","2020","1000 ha","1593","I","Imputed value",""
+"RL","Land Use","108","Burundi","5110","Area","6620","Cropland","2021","2021","1000 ha","1620","I","Imputed value",""
+"RL","Land Use","174","Comoros","5110","Area","6620","Cropland","2018","2018","1000 ha","118","I","Imputed value",""
+"RL","Land Use","174","Comoros","5110","Area","6620","Cropland","2019","2019","1000 ha","118","I","Imputed value",""
+"RL","Land Use","174","Comoros","5110","Area","6620","Cropland","2020","2020","1000 ha","118","I","Imputed value",""
+"RL","Land Use","174","Comoros","5110","Area","6620","Cropland","2021","2021","1000 ha","118","I","Imputed value",""
+"RL","Land Use","262","Djibouti","5110","Area","6620","Cropland","2018","2018","1000 ha","2.8","I","Imputed value",""
+"RL","Land Use","262","Djibouti","5110","Area","6620","Cropland","2019","2019","1000 ha","2.9","I","Imputed value",""
+"RL","Land Use","262","Djibouti","5110","Area","6620","Cropland","2020","2020","1000 ha","3.9","I","Imputed value",""
+"RL","Land Use","262","Djibouti","5110","Area","6620","Cropland","2021","2021","1000 ha","3.9","I","Imputed value",""
+"RL","Land Use","232","Eritrea","5110","Area","6620","Cropland","2018","2018","1000 ha","692","I","Imputed value",""
+"RL","Land Use","232","Eritrea","5110","Area","6620","Cropland","2019","2019","1000 ha","692","I","Imputed value",""
+"RL","Land Use","232","Eritrea","5110","Area","6620","Cropland","2020","2020","1000 ha","692","I","Imputed value",""
+"RL","Land Use","232","Eritrea","5110","Area","6620","Cropland","2021","2021","1000 ha","692","I","Imputed value",""
+"RL","Land Use","231","Ethiopia","5110","Area","6620","Cropland","2018","2018","1000 ha","17903","A","Official figure",""
+"RL","Land Use","231","Ethiopia","5110","Area","6620","Cropland","2019","2019","1000 ha","18190","I","Imputed value",""
+"RL","Land Use","231","Ethiopia","5110","Area","6620","Cropland","2020","2020","1000 ha","18476.1","A","Official figure",""
+"RL","Land Use","231","Ethiopia","5110","Area","6620","Cropland","2021","2021","1000 ha","18595","I","Imputed value",""
+"RL","Land Use","404","Kenya","5110","Area","6620","Cropland","2018","2018","1000 ha","6399","I","Imputed value",""
+"RL","Land Use","404","Kenya","5110","Area","6620","Cropland","2019","2019","1000 ha","6410","I","Imputed value",""
+"RL","Land Use","404","Kenya","5110","Area","6620","Cropland","2020","2020","1000 ha","6410","I","Imputed value",""
+"RL","Land Use","404","Kenya","5110","Area","6620","Cropland","2021","2021","1000 ha","6410","I","Imputed value",""
+"RL","Land Use","450","Madagascar","5110","Area","6620","Cropland","2018","2018","1000 ha","3600","I","Imputed value",""
+"RL","Land Use","450","Madagascar","5110","Area","6620","Cropland","2019","2019","1000 ha","3600","I","Imputed value",""
+"RL","Land Use","450","Madagascar","5110","Area","6620","Cropland","2020","2020","1000 ha","3600","I","Imputed value",""
+"RL","Land Use","450","Madagascar","5110","Area","6620","Cropland","2021","2021","1000 ha","3600","I","Imputed value",""
+"RL","Land Use","454","Malawi","5110","Area","6620","Cropland","2018","2018","1000 ha","4023","I","Imputed value",""
+"RL","Land Use","454","Malawi","5110","Area","6620","Cropland","2019","2019","1000 ha","4078","I","Imputed value",""
+"RL","Land Use","454","Malawi","5110","Area","6620","Cropland","2020","2020","1000 ha","4134","I","Imputed value",""
+"RL","Land Use","454","Malawi","5110","Area","6620","Cropland","2021","2021","1000 ha","4200","I","Imputed value",""
+"RL","Land Use","480","Mauritius","5110","Area","6620","Cropland","2018","2018","1000 ha","79","I","Imputed value",""
+"RL","Land Use","480","Mauritius","5110","Area","6620","Cropland","2019","2019","1000 ha","79","I","Imputed value",""
+"RL","Land Use","480","Mauritius","5110","Area","6620","Cropland","2020","2020","1000 ha","79","I","Imputed value",""
+"RL","Land Use","480","Mauritius","5110","Area","6620","Cropland","2021","2021","1000 ha","79","I","Imputed value",""
+"RL","Land Use","175","Mayotte","5110","Area","6620","Cropland","2018","2018","1000 ha","19.98","A","Official figure",""
+"RL","Land Use","175","Mayotte","5110","Area","6620","Cropland","2019","2019","1000 ha","19.98","A","Official figure",""
+"RL","Land Use","175","Mayotte","5110","Area","6620","Cropland","2020","2020","1000 ha","19.98","A","Official figure",""
+"RL","Land Use","175","Mayotte","5110","Area","6620","Cropland","2021","2021","1000 ha","19.98","I","Imputed value",""
+"RL","Land Use","508","Mozambique","5110","Area","6620","Cropland","2018","2018","1000 ha","5950","I","Imputed value",""
+"RL","Land Use","508","Mozambique","5110","Area","6620","Cropland","2019","2019","1000 ha","5950","I","Imputed value",""
+"RL","Land Use","508","Mozambique","5110","Area","6620","Cropland","2020","2020","1000 ha","5950","I","Imputed value",""
+"RL","Land Use","508","Mozambique","5110","Area","6620","Cropland","2021","2021","1000 ha","5950","I","Imputed value",""
+"RL","Land Use","516","Namibia","5110","Area","6620","Cropland","2018","2018","1000 ha","811","I","Imputed value",""
+"RL","Land Use","516","Namibia","5110","Area","6620","Cropland","2019","2019","1000 ha","811","I","Imputed value",""
+"RL","Land Use","516","Namibia","5110","Area","6620","Cropland","2020","2020","1000 ha","811","I","Imputed value",""
+"RL","Land Use","516","Namibia","5110","Area","6620","Cropland","2021","2021","1000 ha","812","I","Imputed value",""
+"RL","Land Use","638","Réunion","5110","Area","6620","Cropland","2018","2018","1000 ha","37.28","A","Official figure",""
+"RL","Land Use","638","Réunion","5110","Area","6620","Cropland","2019","2019","1000 ha","37.14","A","Official figure",""
+"RL","Land Use","638","Réunion","5110","Area","6620","Cropland","2020","2020","1000 ha","36.82","A","Official figure",""
+"RL","Land Use","638","Réunion","5110","Area","6620","Cropland","2021","2021","1000 ha","36.82","I","Imputed value",""
+"RL","Land Use","646","Rwanda","5110","Area","6620","Cropland","2018","2018","1000 ha","1545","I","Imputed value",""
+"RL","Land Use","646","Rwanda","5110","Area","6620","Cropland","2019","2019","1000 ha","1502","I","Imputed value",""
+"RL","Land Use","646","Rwanda","5110","Area","6620","Cropland","2020","2020","1000 ha","1560.6","I","Imputed value",""
+"RL","Land Use","646","Rwanda","5110","Area","6620","Cropland","2021","2021","1000 ha","1618.4","I","Imputed value",""
+"RL","Land Use","686","Senegal","5110","Area","6620","Cropland","2018","2018","1000 ha","3758","I","Imputed value",""
+"RL","Land Use","686","Senegal","5110","Area","6620","Cropland","2019","2019","1000 ha","3795","I","Imputed value",""
+"RL","Land Use","686","Senegal","5110","Area","6620","Cropland","2020","2020","1000 ha","3830","I","Imputed value",""
+"RL","Land Use","686","Senegal","5110","Area","6620","Cropland","2021","2021","1000 ha","3911","I","Imputed value",""
+"RL","Land Use","690","Seychelles","5110","Area","6620","Cropland","2018","2018","1000 ha","1.55","I","Imputed value",""
+"RL","Land Use","690","Seychelles","5110","Area","6620","Cropland","2019","2019","1000 ha","1.55","I","Imputed value",""
+"RL","Land Use","690","Seychelles","5110","Area","6620","Cropland","2020","2020","1000 ha","1.55","I","Imputed value",""
+"RL","Land Use","690","Seychelles","5110","Area","6620","Cropland","2021","2021","1000 ha","1.55","I","Imputed value",""
+"RL","Land Use","706","Somalia","5110","Area","6620","Cropland","2018","2018","1000 ha","1126","I","Imputed value",""
+"RL","Land Use","706","Somalia","5110","Area","6620","Cropland","2019","2019","1000 ha","1127","I","Imputed value",""
+"RL","Land Use","706","Somalia","5110","Area","6620","Cropland","2020","2020","1000 ha","1128","I","Imputed value",""
+"RL","Land Use","706","Somalia","5110","Area","6620","Cropland","2021","2021","1000 ha","1129","I","Imputed value",""
+"RL","Land Use","728","South Sudan","5110","Area","6620","Cropland","2018","2018","1000 ha","2477.7","I","Imputed value",""
+"RL","Land Use","728","South Sudan","5110","Area","6620","Cropland","2019","2019","1000 ha","2477.7","A","Official figure",""
+"RL","Land Use","728","South Sudan","5110","Area","6620","Cropland","2020","2020","1000 ha","2477.7","A","Official figure",""
+"RL","Land Use","728","South Sudan","5110","Area","6620","Cropland","2021","2021","1000 ha","2479.7","I","Imputed value",""
+"RL","Land Use","729","Sudan","5110","Area","6620","Cropland","2018","2018","1000 ha","21210.84","I","Imputed value",""
+"RL","Land Use","729","Sudan","5110","Area","6620","Cropland","2019","2019","1000 ha","21210.84","I","Imputed value",""
+"RL","Land Use","729","Sudan","5110","Area","6620","Cropland","2020","2020","1000 ha","21210.84","I","Imputed value",""
+"RL","Land Use","729","Sudan","5110","Area","6620","Cropland","2021","2021","1000 ha","21210.84","I","Imputed value",""
+"RL","Land Use","768","Togo","5110","Area","6620","Cropland","2018","2018","1000 ha","2820","I","Imputed value",""
+"RL","Land Use","768","Togo","5110","Area","6620","Cropland","2019","2019","1000 ha","2820","I","Imputed value",""
+"RL","Land Use","768","Togo","5110","Area","6620","Cropland","2020","2020","1000 ha","2820","I","Imputed value",""
+"RL","Land Use","768","Togo","5110","Area","6620","Cropland","2021","2021","1000 ha","2820","I","Imputed value",""
+"RL","Land Use","800","Uganda","5110","Area","6620","Cropland","2018","2018","1000 ha","9100","I","Imputed value",""
+"RL","Land Use","800","Uganda","5110","Area","6620","Cropland","2019","2019","1000 ha","9100","I","Imputed value",""
+"RL","Land Use","800","Uganda","5110","Area","6620","Cropland","2020","2020","1000 ha","9100","I","Imputed value",""
+"RL","Land Use","800","Uganda","5110","Area","6620","Cropland","2021","2021","1000 ha","9100","I","Imputed value",""
+"RL","Land Use","834","United Republic of Tanzania","5110","Area","6620","Cropland","2018","2018","1000 ha","15097.4","I","Imputed value",""
+"RL","Land Use","834","United Republic of Tanzania","5110","Area","6620","Cropland","2019","2019","1000 ha","15309.7","I","Imputed value",""
+"RL","Land Use","834","United Republic of Tanzania","5110","Area","6620","Cropland","2020","2020","1000 ha","15521.2","I","Imputed value",""
+"RL","Land Use","834","United Republic of Tanzania","5110","Area","6620","Cropland","2021","2021","1000 ha","15521.2","I","Imputed value",""
+"RL","Land Use","894","Zambia","5110","Area","6620","Cropland","2018","2018","1000 ha","3839","I","Imputed value",""
+"RL","Land Use","894","Zambia","5110","Area","6620","Cropland","2019","2019","1000 ha","3839","I","Imputed value",""
+"RL","Land Use","894","Zambia","5110","Area","6620","Cropland","2020","2020","1000 ha","3839","I","Imputed value",""
+"RL","Land Use","894","Zambia","5110","Area","6620","Cropland","2021","2021","1000 ha","3839","I","Imputed value",""
+"RL","Land Use","716","Zimbabwe","5110","Area","6620","Cropland","2018","2018","1000 ha","4100","I","Imputed value",""
+"RL","Land Use","716","Zimbabwe","5110","Area","6620","Cropland","2019","2019","1000 ha","4100","I","Imputed value",""
+"RL","Land Use","716","Zimbabwe","5110","Area","6620","Cropland","2020","2020","1000 ha","4100","I","Imputed value",""
+"RL","Land Use","716","Zimbabwe","5110","Area","6620","Cropland","2021","2021","1000 ha","4100","I","Imputed value",""
diff --git a/data/ref_samples_area/ceo-Kenya-Crop-Area-Estimation-Reference-Sample-2019---Set-1-sample-data-2024-03-14.csv b/data/ref_samples_area/ceo-Kenya-Crop-Area-Estimation-Reference-Sample-2019---Set-1-sample-data-2024-03-14.csv
new file mode 100644
index 00000000..4f53ec0e
--- /dev/null
+++ b/data/ref_samples_area/ceo-Kenya-Crop-Area-Estimation-Reference-Sample-2019---Set-1-sample-data-2024-03-14.csv
@@ -0,0 +1,545 @@
+plotid,sampleid,lon,lat,email,flagged,collection_time,analysis_duration,imagery_title,imagery_attributions,sample_geom,pl_sampleid,Does this point correspond to active cropland?
+0,0,34.988629829555656,2.403552919494441,aasareansah@gmail.com,false,2023-03-02 16:50,119.3 secs,Planet Monthly Mosaics,,POINT(34.988629829555656 2.403552919494441),0,Non-crop
+1,1,40.4369134463712,0.23745156614928092,acgins@umd.edu,false,2023-02-28 19:38,138.7 secs,Planet Monthly Mosaics,,POINT(40.4369134463712 0.237451566149281),1,Non-crop
+2,2,35.525785266678824,4.203260690691489,acgins@umd.edu,false,2023-02-28 19:39,54.0 secs,Sentinel-2,,POINT(35.525785266678824 4.203260690691489),2,Non-crop
+3,3,35.34502880181526,1.7861325423989396,acgins@umd.edu,false,2023-02-28 19:48,593.3 secs,Sentinel-2,,POINT(35.34502880181526 1.78613254239894),3,Non-crop
+4,4,41.092915614480624,-2.088706573827987,acgins@umd.edu,false,2023-03-02 21:56,273.1 secs,Planet Monthly Mosaics,,POINT(41.092915614480624 -2.088706573827987),4,Crop
+5,5,34.4249103574835,-1.030559071942206,acgins@umd.edu,false,2023-02-28 20:27,1314.0 secs,Planet Monthly Mosaics,,POINT(34.4249103574835 -1.030559071942206),5,Crop
+6,6,39.74092423396898,0.4906842425498013,acgins@umd.edu,false,2023-02-28 20:34,422.6 secs,Planet Monthly Mosaics,,POINT(39.74092423396898 0.490684242549801),6,Non-crop
+7,7,35.17727471050547,0.8244599235574998,acgins@umd.edu,false,2023-02-28 20:34,38.1 secs,Sentinel-2,,POINT(35.17727471050547 0.8244599235575),7,Crop
+8,8,39.45538305881567,1.1210267423453937,acgins@umd.edu,false,2023-02-28 20:36,88.5 secs,Sentinel-2,,POINT(39.45538305881567 1.121026742345394),8,Non-crop
+9,9,39.369209788785,-2.6825226398269617,acgins@umd.edu,false,2023-02-28 20:37,83.3 secs,Planet Monthly Mosaics,,POINT(39.369209788785 -2.682522639826962),9,Non-crop
+10,10,34.7476597030811,3.302863534933985,acgins@umd.edu,false,2023-02-28 20:44,382.4 secs,Planet Monthly Mosaics,,POINT(34.7476597030811 3.302863534933985),10,Non-crop
+11,11,37.52023980882631,1.1294265556977394,acgins@umd.edu,false,2023-02-28 20:46,111.7 secs,Planet Monthly Mosaics,,POINT(37.52023980882631 1.129426555697739),11,Non-crop
+12,12,40.04232638214461,0.10707860224488248,acgins@umd.edu,false,2023-02-28 20:47,82.9 secs,Planet Monthly Mosaics,,POINT(40.04232638214461 0.107078602244882),12,Non-crop
+13,13,36.59504709600916,2.079095336922312,acgins@umd.edu,false,2023-02-28 20:48,83.9 secs,Planet Monthly Mosaics,,POINT(36.59504709600916 2.079095336922312),13,Non-crop
+14,14,35.355886022749836,-1.7191142947031781,acgins@umd.edu,false,2023-02-28 20:50,83.5 secs,Planet Monthly Mosaics,,POINT(35.355886022749836 -1.719114294703178),14,Non-crop
+15,15,37.94355071711686,0.2736254374888986,acgins@umd.edu,false,2023-02-28 20:52,134.4 secs,Planet Monthly Mosaics,,POINT(37.94355071711686 0.273625437488899),15,Crop
+16,16,35.567931891106554,-0.963807053974099,acgins@umd.edu,false,2023-02-28 20:54,139.0 secs,Sentinel-2,,POINT(35.567931891106554 -0.963807053974099),16,Crop
+17,17,35.2370463385883,0.23539226037375338,acgins@umd.edu,false,2023-02-28 20:55,50.0 secs,Planet Monthly Mosaics,,POINT(35.2370463385883 0.235392260373753),17,Crop
+18,18,34.56192216282922,-0.5423313934257865,acgins@umd.edu,false,2023-02-28 21:08,767.9 secs,Mapbox Satellite,,POINT(34.56192216282922 -0.542331393425786),18,Crop
+19,19,39.91309533587102,0.5405029647849312,acgins@umd.edu,false,2023-02-28 21:14,378.5 secs,Sentinel-2,,POINT(39.91309533587102 0.540502964784931),19,Non-crop
+20,20,37.77622013677315,-3.216077832453689,acgins@umd.edu,false,2023-02-28 21:17,182.3 secs,Mapbox Satellite,,POINT(37.77622013677315 -3.216077832453689),20,Non-crop
+21,21,36.952890064644144,-0.7336035276099032,acgins@umd.edu,false,2023-03-02 21:51,1267.9 secs,Planet Monthly Mosaics,,POINT(36.952890064644144 -0.733603527609903),21,Crop
+22,22,40.81069538939396,3.8142341143013847,aasareansah@gmail.com,false,2023-03-02 16:52,9.3 secs,Planet Monthly Mosaics,,POINT(40.81069538939396 3.814234114301385),22,Non-crop
+23,23,39.31291565564592,-4.292596583003278,aasareansah@gmail.com,false,2023-03-02 16:52,13.0 secs,Planet Monthly Mosaics,,POINT(39.31291565564592 -4.292596583003278),23,Non-crop
+24,24,36.56392047775658,-1.4401680504539716,aasareansah@gmail.com,false,2023-03-02 16:53,17.6 secs,Planet Monthly Mosaics,,POINT(36.56392047775658 -1.440168050453972),24,Non-crop
+25,25,40.761482926934235,1.797142812318858,aasareansah@gmail.com,false,2023-03-02 16:53,8.4 secs,Planet Monthly Mosaics,,POINT(40.761482926934235 1.797142812318858),25,Non-crop
+26,26,35.157061444706144,1.0119532740001498,aasareansah@gmail.com,false,2023-03-02 16:53,44.9 secs,Planet Monthly Mosaics,,POINT(35.157061444706144 1.01195327400015),26,Non-crop
+27,27,34.203510614976835,3.843240810122485,aasareansah@gmail.com,false,2023-03-02 16:54,18.9 secs,Planet Monthly Mosaics,,POINT(34.203510614976835 3.843240810122485),27,Non-crop
+28,28,40.634491221723714,1.4292896941865394,aasareansah@gmail.com,false,2023-03-02 16:54,11.9 secs,Planet Monthly Mosaics,,POINT(40.634491221723714 1.429289694186539),28,Non-crop
+29,29,37.24315858180454,0.18885273302287983,aasareansah@gmail.com,false,2023-03-02 16:54,30.6 secs,Mapbox Satellite,,POINT(37.24315858180454 0.18885273302288),29,Non-crop
+30,30,38.50529869815832,1.3266174643205182,aasareansah@gmail.com,false,2023-03-02 16:55,12.2 secs,Planet Monthly Mosaics,,POINT(38.50529869815832 1.326617464320518),30,Non-crop
+31,31,40.02570086581538,2.052485246929315,aasareansah@gmail.com,false,2023-03-02 16:55,11.6 secs,Planet Monthly Mosaics,,POINT(40.02570086581538 2.052485246929315),31,Non-crop
+32,32,36.08923467501349,2.8152333378024177,aasareansah@gmail.com,false,2023-03-02 16:55,10.4 secs,Planet Monthly Mosaics,,POINT(36.08923467501349 2.815233337802418),32,Non-crop
+33,33,38.25468798085764,-3.293961403681881,aasareansah@gmail.com,false,2023-03-02 16:55,7.0 secs,Planet Monthly Mosaics,,POINT(38.25468798085764 -3.293961403681881),33,Non-crop
+34,34,36.24856761389988,2.978220395675694,aasareansah@gmail.com,false,2023-03-02 16:55,8.1 secs,Planet Monthly Mosaics,,POINT(36.24856761389988 2.978220395675694),34,Non-crop
+35,35,35.08856440679308,-0.610412869850806,aasareansah@gmail.com,false,2023-03-02 16:56,76.0 secs,Planet Monthly Mosaics,,POINT(35.08856440679308 -0.610412869850806),35,Non-crop
+36,36,37.10586175136883,0.6450479726392292,aasareansah@gmail.com,false,2023-03-02 16:57,12.4 secs,Planet Monthly Mosaics,,POINT(37.10586175136883 0.645047972639229),36,Non-crop
+37,37,36.28157017849977,-0.844978485851314,aasareansah@gmail.com,false,2023-03-02 17:06,575.5 secs,Sentinel-2,,POINT(36.28157017849977 -0.844978485851314),37,Crop
+38,38,35.24641350935284,3.1088112936312293,aasareansah@gmail.com,false,2023-03-02 17:06,12.4 secs,Planet Monthly Mosaics,,POINT(35.24641350935284 3.108811293631229),38,Non-crop
+39,39,35.725864381252485,-0.9901508750054767,aasareansah@gmail.com,false,2023-03-02 17:07,34.2 secs,Planet Monthly Mosaics,,POINT(35.725864381252485 -0.990150875005477),39,Crop
+40,40,36.68065734363421,-0.8149776658973387,aasareansah@gmail.com,false,2023-03-02 17:07,32.3 secs,Planet Monthly Mosaics,,POINT(36.68065734363421 -0.814977665897339),40,Non-crop
+41,41,39.78768478044772,3.532698290163748,aasareansah@gmail.com,false,2023-03-02 17:08,13.3 secs,Planet Monthly Mosaics,,POINT(39.78768478044772 3.532698290163748),41,Non-crop
+42,42,34.937338523086936,1.1808045765140807,aasareansah@gmail.com,false,2023-03-02 17:08,30.6 secs,Sentinel-2,,POINT(34.937338523086936 1.180804576514081),42,Crop
+43,43,35.95117962367605,0.11765502410130428,acgins@umd.edu,false,2023-03-02 22:35,1770.6 secs,Planet Monthly Mosaics,,POINT(35.95117962367605 0.117655024101304),43,Crop
+44,44,36.233816518951166,-0.9529835747040568,acgins@umd.edu,false,2023-03-06 22:03,124.3 secs,Mapbox Satellite,,POINT(36.233816518951166 -0.952983574704057),44,Non-crop
+45,45,40.34542919363465,-0.16642752812001055,aasareansah@gmail.com,false,2023-03-02 19:02,10.5 secs,Planet Monthly Mosaics,,POINT(40.34542919363465 -0.166427528120011),45,Non-crop
+46,46,35.109574883126434,1.7949982414224284,aasareansah@gmail.com,false,2023-03-02 19:02,8.4 secs,Planet Monthly Mosaics,,POINT(35.109574883126434 1.794998241422428),46,Non-crop
+47,47,38.10104270259982,2.2770658661008274,aasareansah@gmail.com,false,2023-03-02 19:02,8.7 secs,Planet Monthly Mosaics,,POINT(38.10104270259982 2.277065866100827),47,Non-crop
+48,48,34.93916270476336,-0.5632779940672789,aasareansah@gmail.com,false,2023-03-02 19:03,43.4 secs,Planet Monthly Mosaics,,POINT(34.93916270476336 -0.563277994067279),48,Non-crop
+49,49,35.698150869472485,2.656619125017031,aasareansah@gmail.com,false,2023-03-02 19:03,7.5 secs,Planet Monthly Mosaics,,POINT(35.698150869472485 2.656619125017031),49,Non-crop
+50,50,38.332830459210996,-2.4627477963661004,aasareansah@gmail.com,false,2023-03-02 19:03,7.0 secs,Planet Monthly Mosaics,,POINT(38.332830459210996 -2.4627477963661),50,Non-crop
+51,51,36.384083118334274,4.384630880395623,aasareansah@gmail.com,false,2023-03-02 19:03,6.8 secs,Planet Monthly Mosaics,,POINT(36.384083118334274 4.384630880395623),51,Non-crop
+52,52,35.01471998180397,-0.42170819623595546,acgins@umd.edu,false,2023-03-02 22:38,26.5 secs,Planet Monthly Mosaics,,POINT(35.01471998180397 -0.421708196235955),52,Crop
+53,53,37.47142387421962,2.60955944432736,aasareansah@gmail.com,false,2023-03-02 19:04,7.2 secs,Planet Monthly Mosaics,,POINT(37.47142387421962 2.60955944432736),53,Non-crop
+54,54,37.44533838762773,-1.8552448087267317,aasareansah@gmail.com,false,2023-03-02 19:04,16.4 secs,Mapbox Satellite,,POINT(37.44533838762773 -1.855244808726732),54,Non-crop
+55,55,40.14514392119623,-3.098656053349216,aasareansah@gmail.com,false,2023-03-02 19:04,7.7 secs,Mapbox Satellite,,POINT(40.14514392119623 -3.098656053349216),55,Non-crop
+56,56,38.6594746226374,-3.835613952172189,aasareansah@gmail.com,false,2023-03-02 19:04,18.0 secs,Planet Monthly Mosaics,,POINT(38.6594746226374 -3.835613952172189),56,Non-crop
+57,57,39.02562274024034,0.07077064319861262,aasareansah@gmail.com,false,2023-03-02 19:05,7.7 secs,Planet Monthly Mosaics,,POINT(39.02562274024034 0.070770643198613),57,Non-crop
+58,58,36.02109758574353,3.5366108060174963,aasareansah@gmail.com,false,2023-03-02 19:05,9.3 secs,Planet Monthly Mosaics,,POINT(36.02109758574353 3.536610806017496),58,Non-crop
+59,59,35.06865100448405,1.187649341007699,aasareansah@gmail.com,false,2023-03-02 19:05,18.5 secs,Planet Monthly Mosaics,,POINT(35.06865100448405 1.187649341007699),59,Crop
+60,60,38.263410192690564,2.051383658830262,aasareansah@gmail.com,false,2023-03-02 19:05,8.3 secs,Planet Monthly Mosaics,,POINT(38.263410192690564 2.051383658830262),60,Non-crop
+61,61,40.14898007195484,3.3226177399453816,aasareansah@gmail.com,false,2023-03-02 19:05,17.1 secs,Planet Monthly Mosaics,,POINT(40.14898007195484 3.322617739945382),61,Non-crop
+62,62,37.206652528458754,-1.1530997062886132,acgins@umd.edu,false,2023-03-02 22:41,192.7 secs,Planet Monthly Mosaics,,POINT(37.206652528458754 -1.153099706288613),62,Crop
+63,63,36.33551213412802,-2.0059902859724703,acgins@umd.edu,false,2023-03-02 22:42,83.8 secs,Planet Monthly Mosaics,,POINT(36.33551213412802 -2.00599028597247),63,Non-crop
+64,64,37.81898292849584,2.890470600118884,acgins@umd.edu,false,2023-03-02 22:43,32.4 secs,Mapbox Satellite,,POINT(37.81898292849584 2.890470600118884),64,Non-crop
+65,65,36.964503175730776,-1.8728601229685733,acgins@umd.edu,false,2023-03-06 22:06,178.2 secs,Mapbox Satellite,,POINT(36.964503175730776 -1.872860122968573),65,Non-crop
+66,66,35.432462250513026,-0.21706777965244287,acgins@umd.edu,false,2023-03-02 22:45,49.9 secs,Sentinel-2,,POINT(35.432462250513026 -0.217067779652443),66,Crop
+67,67,35.12381890958649,0.7606469113885553,acgins@umd.edu,false,2023-03-06 22:09,180.7 secs,Mapbox Satellite,,POINT(35.12381890958649 0.760646911388555),67,Crop
+68,68,34.30929368936593,0.47748228001945475,acgins@umd.edu,false,2023-03-02 22:54,215.1 secs,Mapbox Satellite,,POINT(34.30929368936593 0.477482280019455),68,Crop
+69,69,36.6457343594037,-0.49640885252982253,acgins@umd.edu,false,2023-03-02 22:54,35.7 secs,Planet Monthly Mosaics,,POINT(36.6457343594037 -0.496408852529823),69,Non-crop
+70,70,34.226388257459824,-0.6041420114577926,acgins@umd.edu,false,2023-03-02 22:56,107.2 secs,Planet Monthly Mosaics,,POINT(34.226388257459824 -0.604142011457793),70,Crop
+71,71,38.249558852614086,3.1963730568049713,acgins@umd.edu,false,2023-03-02 22:57,38.0 secs,Planet Monthly Mosaics,,POINT(38.249558852614086 3.196373056804971),71,Non-crop
+72,72,35.416853560011795,3.8915106166058218,acgins@umd.edu,false,2023-03-02 22:57,21.4 secs,Planet Monthly Mosaics,,POINT(35.416853560011795 3.891510616605822),72,Non-crop
+73,73,34.793844712321096,1.0897067468642034,acgins@umd.edu,false,2023-03-02 22:59,92.3 secs,Planet Monthly Mosaics,,POINT(34.793844712321096 1.089706746864203),73,Crop
+74,74,35.82894420505845,0.4963590966445787,acgins@umd.edu,false,2023-03-06 22:09,7.4 secs,Mapbox Satellite,,POINT(35.82894420505845 0.496359096644579),74,Non-crop
+75,75,40.86680566262454,2.5510833756481532,acgins@umd.edu,false,2023-03-02 23:01,67.9 secs,Planet Monthly Mosaics,,POINT(40.86680566262454 2.551083375648153),75,Non-crop
+76,76,37.42434808348648,2.172412965946544,acgins@umd.edu,false,2023-03-02 23:01,10.0 secs,Planet Monthly Mosaics,,POINT(37.42434808348648 2.172412965946544),76,Non-crop
+77,77,37.15129430727518,-0.7226093010817065,acgins@umd.edu,false,2023-03-06 22:11,95.6 secs,Mapbox Satellite,,POINT(37.15129430727518 -0.722609301081706),77,Non-crop
+78,78,35.24400762282487,-0.8344837781524341,acgins@umd.edu,false,2023-03-02 23:03,54.7 secs,Sentinel-2,,POINT(35.24400762282487 -0.834483778152434),78,Crop
+79,79,35.89383258961963,-0.3086155983480622,acgins@umd.edu,false,2023-03-04 19:09,25.1 secs,Planet Monthly Mosaics,,POINT(35.89383258961963 -0.308615598348062),79,Crop
+80,80,34.8374317772852,1.2105020095980699,acgins@umd.edu,false,2023-03-04 19:10,16.7 secs,Planet Monthly Mosaics,,POINT(34.8374317772852 1.21050200959807),80,Crop
+81,81,37.47851943218349,-1.5673861189284093,acgins@umd.edu,false,2023-03-04 19:13,182.4 secs,Planet Monthly Mosaics,,POINT(37.47851943218349 -1.567386118928409),81,Crop
+82,82,37.62528047925142,-3.386462077890213,acgins@umd.edu,false,2023-03-04 19:14,58.1 secs,Planet Monthly Mosaics,,POINT(37.62528047925142 -3.386462077890213),82,Crop
+83,83,37.94051901411475,0.003977377954060247,acgins@umd.edu,false,2023-03-04 19:14,43.1 secs,Mapbox Satellite,,POINT(37.94051901411475 0.00397737795406),83,Crop
+84,84,38.7070591354366,-2.0059845289306772,acgins@umd.edu,false,2023-03-04 19:15,22.4 secs,Planet Monthly Mosaics,,POINT(38.7070591354366 -2.005984528930677),84,Non-crop
+85,85,34.838779927856656,-0.86257077504155,acgins@umd.edu,false,2023-03-04 19:19,285.8 secs,Planet Monthly Mosaics,,POINT(34.838779927856656 -0.86257077504155),85,Crop
+86,86,37.44660412263658,1.2193124117332885,acgins@umd.edu,false,2023-03-04 19:20,16.2 secs,Planet Monthly Mosaics,,POINT(37.44660412263658 1.219312411733288),86,Non-crop
+87,87,34.6632601985172,0.07559985882881662,acgins@umd.edu,false,2023-03-04 19:22,117.0 secs,Planet Monthly Mosaics,,POINT(34.6632601985172 0.075599858828817),87,Non-crop
+88,88,34.638949502003825,0.6729112014973999,acgins@umd.edu,false,2023-03-04 19:22,20.9 secs,Planet Monthly Mosaics,,POINT(34.638949502003825 0.6729112014974),88,Crop
+89,89,34.5994023707549,0.7630158568229832,acgins@umd.edu,false,2023-03-04 19:23,35.1 secs,Planet Monthly Mosaics,,POINT(34.5994023707549 0.763015856822983),89,Crop
+90,90,36.93706274251377,2.255505688906279,acgins@umd.edu,false,2023-03-04 19:23,39.5 secs,Mapbox Satellite,,POINT(36.93706274251377 2.255505688906279),90,Non-crop
+91,91,40.44223583641046,4.03082660667592,acgins@umd.edu,false,2023-03-04 19:24,26.3 secs,Planet Monthly Mosaics,,POINT(40.44223583641046 4.03082660667592),91,Non-crop
+92,92,36.06039300271393,-0.5684581218619031,acgins@umd.edu,false,2023-03-04 19:24,32.5 secs,Mapbox Satellite,,POINT(36.06039300271393 -0.568458121861903),92,Crop
+93,93,37.59588686792248,0.11931919114708699,acgins@umd.edu,false,2023-03-04 19:25,33.1 secs,Planet Monthly Mosaics,,POINT(37.59588686792248 0.119319191147087),93,Non-crop
+94,94,34.95006733497294,-0.7711616777128759,acgins@umd.edu,false,2023-03-06 22:16,311.0 secs,Planet Monthly Mosaics,,POINT(34.95006733497294 -0.771161677712876),94,Crop
+95,95,34.37280204804135,-0.5213450533332684,acgins@umd.edu,false,2023-03-06 21:13,139.4 secs,,,POINT(34.37280204804135 -0.521345053333268),95,Crop
+96,96,35.92793233525755,-2.0132818618461816,acgins@umd.edu,false,2023-03-06 21:13,35.3 secs,Planet Monthly Mosaics,,POINT(35.92793233525755 -2.013281861846182),96,Non-crop
+97,97,40.50489472621563,-1.7350862750790679,acgins@umd.edu,false,2023-03-06 21:14,53.6 secs,Planet Monthly Mosaics,,POINT(40.50489472621563 -1.735086275079068),97,Non-crop
+98,98,34.84343325317191,-0.5513431781255153,acgins@umd.edu,false,2023-03-06 21:15,41.8 secs,Planet Monthly Mosaics,,POINT(34.84343325317191 -0.551343178125515),98,Crop
+99,99,38.810924566797794,2.4564363287855104,acgins@umd.edu,false,2023-03-06 21:15,24.7 secs,Planet Monthly Mosaics,,POINT(38.810924566797794 2.45643632878551),99,Non-crop
+100,100,38.82801401094352,0.44583460966597566,acgins@umd.edu,false,2023-03-06 21:16,15.8 secs,Planet Monthly Mosaics,,POINT(38.82801401094352 0.445834609665976),100,Non-crop
+101,101,35.01842360551604,1.951978852908563,acgins@umd.edu,false,2023-03-06 21:16,19.8 secs,Planet Monthly Mosaics,,POINT(35.01842360551604 1.951978852908563),101,Non-crop
+102,102,38.63846258841169,-1.8744628832696686,acgins@umd.edu,false,2023-03-06 21:17,82.1 secs,Planet Monthly Mosaics,,POINT(38.63846258841169 -1.874462883269669),102,Non-crop
+103,103,34.67637175915808,4.509081535107014,acgins@umd.edu,false,2023-03-06 21:18,35.9 secs,Planet Monthly Mosaics,,POINT(34.67637175915808 4.509081535107014),103,Non-crop
+104,104,34.80059714936316,-0.6330272098164963,acgins@umd.edu,false,2023-03-06 21:20,94.9 secs,Planet Monthly Mosaics,,POINT(34.80059714936316 -0.633027209816496),104,Crop
+105,105,39.03989200445923,-2.346187888919231,acgins@umd.edu,false,2023-03-06 21:21,83.9 secs,Planet Monthly Mosaics,,POINT(39.03989200445923 -2.346187888919231),105,Non-crop
+106,106,37.75101158485352,-0.05595061740159739,acgins@umd.edu,false,2023-03-06 21:23,131.6 secs,Planet Monthly Mosaics,,POINT(37.75101158485352 -0.055950617401597),106,Crop
+107,107,34.94705324098189,-0.5166194835899087,acgins@umd.edu,false,2023-03-06 21:24,49.1 secs,Planet Monthly Mosaics,,POINT(34.94705324098189 -0.516619483589909),107,Crop
+108,108,36.43853260211107,3.97061768786652,acgins@umd.edu,false,2023-03-06 21:24,9.9 secs,Mapbox Satellite,,POINT(36.43853260211107 3.97061768786652),108,Non-crop
+109,109,38.8861466691318,3.0445834421692615,acgins@umd.edu,false,2023-03-06 21:25,23.7 secs,Planet Monthly Mosaics,,POINT(38.8861466691318 3.044583442169262),109,Non-crop
+110,110,35.876614895369954,3.7678862374880135,acgins@umd.edu,false,2023-03-06 21:25,33.9 secs,Planet Monthly Mosaics,,POINT(35.876614895369954 3.767886237488014),110,Non-crop
+111,111,38.82883825639645,2.5306209549193475,acgins@umd.edu,false,2023-03-06 21:33,496.2 secs,Planet Monthly Mosaics,,POINT(38.82883825639645 2.530620954919348),111,Non-crop
+112,112,38.2367270952654,-1.3301300190469625,acgins@umd.edu,false,2023-03-06 22:17,55.9 secs,Mapbox Satellite,,POINT(38.2367270952654 -1.330130019046962),112,Non-crop
+113,113,37.63554202932443,0.03299322115736923,acgins@umd.edu,false,2023-03-06 21:39,216.8 secs,Planet Monthly Mosaics,,POINT(37.63554202932443 0.032993221157369),113,Crop
+114,114,37.05135107858711,-0.388438674158326,acgins@umd.edu,false,2023-03-06 21:41,138.7 secs,Planet Monthly Mosaics,,POINT(37.05135107858711 -0.388438674158326),114,Crop
+115,115,38.39683562140306,-3.562375659885144,acgins@umd.edu,false,2023-03-06 21:42,34.6 secs,Planet Monthly Mosaics,,POINT(38.39683562140306 -3.562375659885144),115,Crop
+116,116,38.47807537401632,-1.3194672935579193,acgins@umd.edu,false,2023-03-06 22:23,189.1 secs,Planet Monthly Mosaics,,POINT(38.47807537401632 -1.319467293557919),116,Non-crop
+117,117,37.90724334671853,1.2126009407086038,acgins@umd.edu,false,2023-03-06 21:45,8.3 secs,Planet Monthly Mosaics,,POINT(37.90724334671853 1.212600940708604),117,Non-crop
+118,118,35.1493469381087,0.7857613956282058,acgins@umd.edu,false,2023-03-06 21:47,87.4 secs,Planet Monthly Mosaics,,POINT(35.1493469381087 0.785761395628206),118,Crop
+119,119,37.04107541888969,2.415639616416417,acgins@umd.edu,false,2023-03-06 21:47,40.4 secs,Planet Monthly Mosaics,,POINT(37.04107541888969 2.415639616416417),119,Non-crop
+120,120,37.135914893319935,0.8892938871269861,acgins@umd.edu,false,2023-03-06 21:48,8.6 secs,Planet Monthly Mosaics,,POINT(37.135914893319935 0.889293887126986),120,Non-crop
+121,121,34.280251153267145,-0.8857188443090579,acgins@umd.edu,false,2023-03-06 21:49,90.0 secs,Sentinel-2,,POINT(34.280251153267145 -0.885718844309058),121,Crop
+122,122,37.104919656283776,0.944772577359767,acgins@umd.edu,false,2023-03-06 21:50,37.8 secs,Planet Monthly Mosaics,,POINT(37.104919656283776 0.944772577359767),122,Non-crop
+123,123,36.91642788302333,-1.8734338149965883,acgins@umd.edu,false,2023-03-06 21:50,28.5 secs,Mapbox Satellite,,POINT(36.91642788302333 -1.873433814996588),123,Non-crop
+124,124,35.6530472090822,1.3337481691783033,acgins@umd.edu,false,2023-03-06 21:53,188.2 secs,Planet Monthly Mosaics,,POINT(35.6530472090822 1.333748169178303),124,Crop
+125,125,37.665284200034286,-0.8743618235367431,acgins@umd.edu,false,2023-03-06 21:56,174.0 secs,Sentinel-2,,POINT(37.665284200034286 -0.874361823536743),125,Non-crop
+126,126,40.28931527278772,-1.6826194889901331,acgins@umd.edu,false,2023-03-06 21:57,33.0 secs,Planet Monthly Mosaics,,POINT(40.28931527278772 -1.682619488990133),126,Non-crop
+127,127,35.24886337281288,0.9780628088660805,acgins@umd.edu,false,2023-03-06 21:57,15.6 secs,Planet Monthly Mosaics,,POINT(35.24886337281288 0.97806280886608),127,Crop
+128,128,35.350804743305915,-0.14292567584629862,acgins@umd.edu,false,2023-03-06 21:59,99.4 secs,Planet Monthly Mosaics,,POINT(35.350804743305915 -0.142925675846299),128,Crop
+129,129,36.875372636897794,-1.9178813516888036,acgins@umd.edu,false,2023-03-06 21:59,37.4 secs,Planet Monthly Mosaics,,POINT(36.875372636897794 -1.917881351688804),129,Non-crop
+130,130,36.36696602805077,-1.0011713498446717,acgins@umd.edu,false,2023-03-06 22:01,87.2 secs,Mapbox Satellite,,POINT(36.36696602805077 -1.001171349844672),130,Non-crop
+131,131,34.03567997149679,-0.45059458138419733,acgins@umd.edu,false,2023-03-06 22:30,453.4 secs,Planet Monthly Mosaics,,POINT(34.03567997149679 -0.450594581384197),131,Non-crop
+132,132,34.31052408060243,0.06011131997460628,acgins@umd.edu,false,2023-03-06 22:31,21.8 secs,Mapbox Satellite,,POINT(34.31052408060243 0.060111319974606),132,Non-crop
+133,133,40.112200611852735,3.057621068313114,acgins@umd.edu,false,2023-03-06 22:31,20.7 secs,Mapbox Satellite,,POINT(40.112200611852735 3.057621068313114),133,Non-crop
+134,134,37.59446156272681,-0.6348207832764996,acgins@umd.edu,false,2023-03-06 22:38,440.2 secs,Planet Monthly Mosaics,,POINT(37.59446156272681 -0.6348207832765),134,Non-crop
+135,135,40.9961063143978,-1.6779669321206159,acgins@umd.edu,false,2023-03-06 22:46,481.6 secs,Planet Monthly Mosaics,,POINT(40.9961063143978 -1.677966932120616),135,Non-crop
+136,136,39.22212647015637,2.6922268323855563,acgins@umd.edu,false,2023-03-06 22:47,40.9 secs,Planet Monthly Mosaics,,POINT(39.22212647015637 2.692226832385556),136,Non-crop
+137,137,36.21832154143681,-0.33166413329927724,acgins@umd.edu,false,2023-03-06 22:48,43.8 secs,Planet Monthly Mosaics,,POINT(36.21832154143681 -0.331664133299277),137,Crop
+138,138,34.51475222889342,-0.7822238210290815,acgins@umd.edu,false,2023-03-06 22:48,35.5 secs,Planet Monthly Mosaics,,POINT(34.51475222889342 -0.782223821029082),138,Crop
+139,139,38.50386682037691,-0.7440523952127349,acgins@umd.edu,false,2023-03-06 22:49,29.5 secs,Planet Monthly Mosaics,,POINT(38.50386682037691 -0.744052395212735),139,Non-crop
+140,140,34.544980328359806,3.3127536927240926,acgins@umd.edu,false,2023-03-06 22:49,23.1 secs,Mapbox Satellite,,POINT(34.544980328359806 3.312753692724093),140,Non-crop
+141,141,35.072614057925755,2.0515640292935595,acgins@umd.edu,false,2023-03-06 22:50,31.9 secs,Planet Monthly Mosaics,,POINT(35.072614057925755 2.05156402929356),141,Non-crop
+142,142,37.63731851488094,-0.7082741543121748,acgins@umd.edu,false,2023-03-06 22:52,110.3 secs,Mapbox Satellite,,POINT(37.63731851488094 -0.708274154312175),142,Crop
+143,143,36.79356032174582,-1.3320484579772995,acgins@umd.edu,false,2023-03-06 22:52,12.9 secs,Mapbox Satellite,,POINT(36.79356032174582 -1.3320484579773),143,Non-crop
+144,144,37.196987405380554,-0.997502551481028,acgins@umd.edu,false,2023-03-06 22:52,29.5 secs,Planet Monthly Mosaics,,POINT(37.196987405380554 -0.997502551481028),144,Crop
+145,145,35.97558762159962,-0.12276447907124412,acgins@umd.edu,false,2023-03-06 22:53,16.7 secs,Planet Monthly Mosaics,,POINT(35.97558762159962 -0.122764479071244),145,Crop
+146,146,34.1891721541313,-0.6146176829171154,acgins@umd.edu,false,2023-03-06 22:54,105.1 secs,Planet Monthly Mosaics,,POINT(34.1891721541313 -0.614617682917115),146,Crop
+147,147,36.92847527942525,1.2782331263275961,acgins@umd.edu,false,2023-03-06 22:55,21.4 secs,Planet Monthly Mosaics,,POINT(36.92847527942525 1.278233126327596),147,Non-crop
+148,148,37.67795253138457,-0.29646401880195755,acgins@umd.edu,false,2023-03-06 22:56,100.1 secs,Mapbox Satellite,,POINT(37.67795253138457 -0.296464018801958),148,Crop
+149,149,38.06462053176975,-1.043559942579206,acgins@umd.edu,false,2023-03-06 22:57,48.4 secs,Planet Monthly Mosaics,,POINT(38.06462053176975 -1.043559942579206),149,Crop
+150,150,34.886305931598756,1.5162521774427242,acgins@umd.edu,false,2023-03-06 23:08,628.7 secs,Planet Monthly Mosaics,,POINT(34.886305931598756 1.516252177442724),150,Non-crop
+151,151,36.96408686642167,0.4599662921332445,acgins@umd.edu,false,2023-03-06 23:09,51.0 secs,Planet Monthly Mosaics,,POINT(36.96408686642167 0.459966292133244),151,Non-crop
+152,152,35.13520286056352,-1.1297140056307589,acgins@umd.edu,false,2023-03-06 23:10,92.2 secs,Planet Monthly Mosaics,,POINT(35.13520286056352 -1.129714005630759),152,Crop
+153,153,41.800681225973825,3.9420204760538824,acgins@umd.edu,false,2023-03-06 23:11,32.5 secs,Planet Monthly Mosaics,,POINT(41.800681225973825 3.942020476053882),153,Non-crop
+154,154,36.41868031095638,-1.6763774091068298,acgins@umd.edu,false,2023-03-06 23:11,38.6 secs,Planet Monthly Mosaics,,POINT(36.41868031095638 -1.67637740910683),154,Non-crop
+155,155,38.033900036116925,-1.3282559996083236,acgins@umd.edu,false,2023-03-06 23:16,278.6 secs,Planet Monthly Mosaics,,POINT(38.033900036116925 -1.328255999608324),155,Non-crop
+156,156,39.29015146450414,-4.3996825162698165,acgins@umd.edu,false,2023-03-06 23:19,187.0 secs,Sentinel-2,,POINT(39.29015146450414 -4.399682516269816),156,Crop
+157,157,37.44623545167376,-1.0728680549928664,acgins@umd.edu,false,2023-03-06 23:21,100.7 secs,Planet Monthly Mosaics,,POINT(37.44623545167376 -1.072868054992866),157,Non-crop
+158,158,40.00198076172898,-1.4034050963748328,acgins@umd.edu,false,2023-03-06 23:21,11.9 secs,Planet Monthly Mosaics,,POINT(40.00198076172898 -1.403405096374833),158,Crop
+159,159,34.51540863900097,0.20085040451937342,acgins@umd.edu,false,2023-03-06 23:21,8.2 secs,Planet Monthly Mosaics,,POINT(34.51540863900097 0.200850404519373),159,Non-crop
+160,160,34.364068159964674,-0.027030376910205087,acgins@umd.edu,false,2023-03-06 23:22,27.7 secs,Planet Monthly Mosaics,,POINT(34.364068159964674 -0.027030376910205),160,Crop
+161,161,38.97895604283259,2.4386926635852486,acgins@umd.edu,false,2023-03-06 23:22,10.7 secs,Planet Monthly Mosaics,,POINT(38.97895604283259 2.438692663585249),161,Non-crop
+162,162,35.27387999609261,0.11199548140903959,acgins@umd.edu,false,2023-03-06 23:22,19.9 secs,Planet Monthly Mosaics,,POINT(35.27387999609261 0.11199548140904),162,Crop
+163,163,39.911542341177416,-2.5611092030692055,acgins@umd.edu,false,2023-03-06 23:22,27.7 secs,Mapbox Satellite,,POINT(39.911542341177416 -2.561109203069206),163,Non-crop
+164,164,35.185079473396854,0.5617606376305225,acgins@umd.edu,false,2023-03-06 23:23,16.7 secs,Planet Monthly Mosaics,,POINT(35.185079473396854 0.561760637630522),164,Crop
+165,165,35.05688885441114,-0.6795407745893438,acgins@umd.edu,false,2023-03-06 23:25,152.2 secs,Planet Monthly Mosaics,,POINT(35.05688885441114 -0.679540774589344),165,Crop
+166,166,37.58793262053442,3.7690288499729587,acgins@umd.edu,false,2023-03-06 23:26,14.9 secs,Mapbox Satellite,,POINT(37.58793262053442 3.769028849972959),166,Non-crop
+167,167,34.45875563125551,0.6272795031255634,acgins@umd.edu,false,2023-03-06 23:26,40.6 secs,Mapbox Satellite,,POINT(34.45875563125551 0.627279503125563),167,Crop
+168,168,34.40926954593081,0.2551783003635253,aasareansah@gmail.com,false,2023-03-07 15:02,36.9 secs,Planet Monthly Mosaics,,POINT(34.40926954593081 0.255178300363525),168,Non-crop
+169,169,39.660802154481644,-3.190642695064495,aasareansah@gmail.com,false,2023-03-07 15:02,13.8 secs,Planet Monthly Mosaics,,POINT(39.660802154481644 -3.190642695064495),169,Non-crop
+170,170,34.54046798139366,0.6859414994541169,aasareansah@gmail.com,false,2023-03-07 15:03,32.4 secs,Planet Monthly Mosaics,,POINT(34.54046798139366 0.685941499454117),170,Crop
+171,171,39.073614246897385,-0.2633723289888229,aasareansah@gmail.com,false,2023-03-07 15:03,12.4 secs,Planet Monthly Mosaics,,POINT(39.073614246897385 -0.263372328988823),171,Non-crop
+172,172,38.432919130857414,1.7396830042557871,aasareansah@gmail.com,false,2023-03-07 15:03,9.2 secs,Planet Monthly Mosaics,,POINT(38.432919130857414 1.739683004255787),172,Non-crop
+173,173,37.095575381750336,-1.0096069277601003,aasareansah@gmail.com,false,2023-03-07 15:03,15.0 secs,Planet Monthly Mosaics,,POINT(37.095575381750336 -1.0096069277601),173,Non-crop
+174,174,36.65947410135244,-1.1671998161925197,aasareansah@gmail.com,false,2023-03-07 15:04,24.3 secs,Planet Monthly Mosaics,,POINT(36.65947410135244 -1.16719981619252),174,Crop
+175,175,34.45338777219258,-0.7355236000562643,aasareansah@gmail.com,false,2023-03-07 15:04,9.5 secs,Planet Monthly Mosaics,,POINT(34.45338777219258 -0.735523600056264),175,Non-crop
+176,176,35.91774080987921,-1.0793531043719524,aasareansah@gmail.com,false,2023-03-07 15:04,13.8 secs,Planet Monthly Mosaics,,POINT(35.91774080987921 -1.079353104371952),176,Non-crop
+177,177,34.72286953140142,0.4356879558642015,aasareansah@gmail.com,false,2023-03-07 15:04,14.1 secs,Planet Monthly Mosaics,,POINT(34.72286953140142 0.435687955864202),177,Non-crop
+178,178,35.247612435415604,1.055323072725927,aasareansah@gmail.com,false,2023-03-07 15:04,19.3 secs,Planet Monthly Mosaics,,POINT(35.247612435415604 1.055323072725927),178,Crop
+179,179,34.17397768556119,0.41298920794555216,aasareansah@gmail.com,false,2023-03-07 15:05,14.7 secs,Planet Monthly Mosaics,,POINT(34.17397768556119 0.412989207945552),179,Non-crop
+180,180,36.90739764160443,-0.6045908967298282,aasareansah@gmail.com,false,2023-03-07 15:05,18.5 secs,Planet Monthly Mosaics,,POINT(36.90739764160443 -0.604590896729828),180,Non-crop
+181,181,35.09232298964106,-0.24405121259209608,aasareansah@gmail.com,false,2023-03-07 15:06,66.5 secs,Planet Monthly Mosaics,,POINT(35.09232298964106 -0.244051212592096),181,Non-crop
+182,182,34.93320904860172,3.089392923071744,aasareansah@gmail.com,false,2023-03-07 15:06,15.0 secs,Planet Monthly Mosaics,,POINT(34.93320904860172 3.089392923071744),182,Non-crop
+183,183,35.386016558505396,0.7061819098528996,aasareansah@gmail.com,false,2023-03-07 15:06,20.2 secs,Planet Monthly Mosaics,,POINT(35.386016558505396 0.7061819098529),183,Crop
+184,184,35.44600492019322,0.6737515821752349,acgins@umd.edu,false,2023-03-07 20:48,266.8 secs,Planet Monthly Mosaics,,POINT(35.44600492019322 0.673751582175235),184,Non-crop
+185,185,36.83715746471805,-0.3040771550035826,aasareansah@gmail.com,false,2023-03-07 15:08,22.3 secs,Planet Monthly Mosaics,,POINT(36.83715746471805 -0.304077155003583),185,Crop
+186,186,35.47235817550058,-1.7747537021443485,aasareansah@gmail.com,false,2023-03-07 15:08,9.6 secs,Planet Monthly Mosaics,,POINT(35.47235817550058 -1.774753702144348),186,Non-crop
+187,187,35.9969624367669,-0.05057581718672911,acgins@umd.edu,false,2023-03-07 20:57,561.7 secs,Planet Monthly Mosaics,,POINT(35.9969624367669 -0.050575817186729),187,Crop
+188,188,35.02138764947741,0.9068522061088463,aasareansah@gmail.com,false,2023-03-07 15:09,16.1 secs,Planet Monthly Mosaics,,POINT(35.02138764947741 0.906852206108846),188,Non-crop
+189,189,37.28290916070205,-0.5670627725341534,acgins@umd.edu,false,2023-03-07 21:03,314.8 secs,Planet Monthly Mosaics,,POINT(37.28290916070205 -0.567062772534153),189,Non-crop
+190,190,36.27531089330441,1.6312781626526536,aasareansah@gmail.com,false,2023-03-07 15:11,9.3 secs,Planet Monthly Mosaics,,POINT(36.27531089330441 1.631278162652654),190,Non-crop
+191,191,38.18613982888146,2.3095627412092896,aasareansah@gmail.com,false,2023-03-07 15:11,11.6 secs,Planet Monthly Mosaics,,POINT(38.18613982888146 2.30956274120929),191,Non-crop
+192,192,38.82823907086845,2.961020075278439,aasareansah@gmail.com,false,2023-03-07 15:11,10.1 secs,Planet Monthly Mosaics,,POINT(38.82823907086845 2.961020075278439),192,Non-crop
+193,193,35.756354105431015,-0.25214860600974354,aasareansah@gmail.com,false,2023-03-07 15:11,14.7 secs,Planet Monthly Mosaics,,POINT(35.756354105431015 -0.252148606009744),193,Crop
+194,194,34.05911892769959,-0.7668905312894166,aasareansah@gmail.com,false,2023-03-07 15:12,38.0 secs,Planet Monthly Mosaics,,POINT(34.05911892769959 -0.766890531289417),194,Non-crop
+195,195,37.434398228197466,-1.4124646108398355,aasareansah@gmail.com,false,2023-03-07 15:12,16.4 secs,Planet Monthly Mosaics,,POINT(37.434398228197466 -1.412464610839836),195,Non-crop
+196,196,34.24813006085403,-0.18754586249713845,aasareansah@gmail.com,false,2023-03-07 15:13,19.2 secs,Planet Monthly Mosaics,,POINT(34.24813006085403 -0.187545862497138),196,Non-crop
+197,197,39.38163103579628,-0.5870464083569815,aasareansah@gmail.com,false,2023-03-07 15:13,13.3 secs,Planet Monthly Mosaics,,POINT(39.38163103579628 -0.587046408356982),197,Non-crop
+198,198,34.407612059311866,4.020736333189546,aasareansah@gmail.com,false,2023-03-07 15:13,9.6 secs,Planet Monthly Mosaics,,POINT(34.407612059311866 4.020736333189546),198,Non-crop
+199,199,34.5106962080211,-0.1159075048798257,acgins@umd.edu,false,2023-03-07 21:04,62.6 secs,Mapbox Satellite,,POINT(34.5106962080211 -0.115907504879826),199,Crop
+200,200,39.699070498363014,0.8268795354015316,aasareansah@gmail.com,false,2023-03-07 15:14,17.0 secs,Planet Monthly Mosaics,,POINT(39.699070498363014 0.826879535401532),200,Non-crop
+201,201,35.26606385102699,-0.8488962562008477,acgins@umd.edu,false,2023-03-07 21:09,306.0 secs,Planet Monthly Mosaics,,POINT(35.26606385102699 -0.848896256200848),201,Crop
+202,202,37.00569584470588,-0.4607649473863657,aasareansah@gmail.com,false,2023-03-07 15:15,26.7 secs,Planet Monthly Mosaics,,POINT(37.00569584470588 -0.460764947386366),202,Non-crop
+203,203,34.681833787046784,-0.0017856800434060153,aasareansah@gmail.com,false,2023-03-07 15:16,56.4 secs,Planet Monthly Mosaics,,POINT(34.681833787046784 -0.001785680043406),203,Non-crop
+204,204,35.00322143113498,-0.37269709187609834,aasareansah@gmail.com,false,2023-03-07 15:16,14.3 secs,Planet Monthly Mosaics,,POINT(35.00322143113498 -0.372697091876098),204,Non-crop
+205,205,35.09439243911183,0.4304058574197333,aasareansah@gmail.com,false,2023-03-07 15:17,62.4 secs,Planet Monthly Mosaics,,POINT(35.09439243911183 0.430405857419733),205,Crop
+206,206,35.13586471270375,3.8666126635096743,aasareansah@gmail.com,false,2023-03-07 15:17,13.6 secs,Planet Monthly Mosaics,,POINT(35.13586471270375 3.866612663509674),206,Non-crop
+207,207,38.15212804664146,3.3677157950076575,aasareansah@gmail.com,false,2023-03-07 15:17,9.7 secs,Planet Monthly Mosaics,,POINT(38.15212804664146 3.367715795007658),207,Non-crop
+208,208,38.286578480177624,-3.580862928287623,aasareansah@gmail.com,false,2023-03-07 15:17,8.5 secs,Planet Monthly Mosaics,,POINT(38.286578480177624 -3.580862928287623),208,Non-crop
+209,209,39.404093137783995,2.45152108353291,aasareansah@gmail.com,false,2023-03-07 15:17,11.3 secs,Planet Monthly Mosaics,,POINT(39.404093137783995 2.45152108353291),209,Non-crop
+210,210,35.065711700959476,2.547656684794862,aasareansah@gmail.com,false,2023-03-07 15:18,9.6 secs,Planet Monthly Mosaics,,POINT(35.065711700959476 2.547656684794862),210,Non-crop
+211,211,35.947769272904495,0.20415397808837918,aasareansah@gmail.com,false,2023-03-07 15:18,21.9 secs,Planet Monthly Mosaics,,POINT(35.947769272904495 0.204153978088379),211,Non-crop
+212,212,37.91801786296307,-2.1375884435327785,aasareansah@gmail.com,false,2023-03-07 15:19,51.9 secs,Planet Monthly Mosaics,,POINT(37.91801786296307 -2.137588443532778),212,Non-crop
+213,213,36.88669703425683,0.42706564857485335,aasareansah@gmail.com,false,2023-03-07 15:19,9.7 secs,Planet Monthly Mosaics,,POINT(36.88669703425683 0.427065648574853),213,Non-crop
+214,214,34.068160039789994,-0.6537499847545581,aasareansah@gmail.com,false,2023-03-07 15:19,15.2 secs,Planet Monthly Mosaics,,POINT(34.068160039789994 -0.653749984754558),214,Crop
+215,215,37.99915424752388,0.06625355062429386,aasareansah@gmail.com,false,2023-03-07 15:20,34.6 secs,Planet Monthly Mosaics,,POINT(37.99915424752388 0.066253550624294),215,Non-crop
+216,216,38.23840190779963,1.666609091145675,aasareansah@gmail.com,false,2023-03-07 15:20,15.7 secs,Planet Monthly Mosaics,,POINT(38.23840190779963 1.666609091145675),216,Non-crop
+217,217,38.09419036742432,2.9609319691321545,aasareansah@gmail.com,false,2023-03-07 15:20,8.8 secs,Planet Monthly Mosaics,,POINT(38.09419036742432 2.960931969132154),217,Non-crop
+218,218,40.11268741129131,0.07066709430285514,aasareansah@gmail.com,false,2023-03-07 15:20,8.0 secs,Planet Monthly Mosaics,,POINT(40.11268741129131 0.070667094302855),218,Non-crop
+219,219,35.33297997639196,-0.8617340640007177,aasareansah@gmail.com,false,2023-03-07 15:20,31.1 secs,Planet Monthly Mosaics,,POINT(35.33297997639196 -0.861734064000718),219,Crop
+220,220,35.85807814705425,4.10000582662628,aasareansah@gmail.com,false,2023-03-07 15:21,9.3 secs,Planet Monthly Mosaics,,POINT(35.85807814705425 4.10000582662628),220,Non-crop
+221,221,34.5729975776531,-0.6466759300640075,aasareansah@gmail.com,false,2023-03-07 15:21,12.1 secs,Planet Monthly Mosaics,,POINT(34.5729975776531 -0.646675930064008),221,Crop
+222,222,40.546244593875265,1.1821031768260064,aasareansah@gmail.com,false,2023-03-07 15:21,9.8 secs,Planet Monthly Mosaics,,POINT(40.546244593875265 1.182103176826006),222,Non-crop
+223,223,37.548413950439276,-0.41160735763497225,aasareansah@gmail.com,false,2023-03-07 15:21,32.9 secs,Planet Monthly Mosaics,,POINT(37.548413950439276 -0.411607357634972),223,Non-crop
+224,224,35.75968549281819,-0.9621064827168123,acgins@umd.edu,false,2023-03-07 21:09,26.7 secs,Planet Monthly Mosaics,,POINT(35.75968549281819 -0.962106482716812),224,Crop
+225,225,35.074744972874576,-0.7925917814780629,aasareansah@gmail.com,false,2023-03-07 15:24,43.2 secs,Planet Monthly Mosaics,,POINT(35.074744972874576 -0.792591781478063),225,Crop
+226,226,34.46024019008391,3.53618931300853,aasareansah@gmail.com,false,2023-03-07 15:24,9.3 secs,Planet Monthly Mosaics,,POINT(34.46024019008391 3.53618931300853),226,Non-crop
+227,227,38.83677570935286,2.824285402842024,aasareansah@gmail.com,false,2023-03-07 15:24,9.5 secs,Planet Monthly Mosaics,,POINT(38.83677570935286 2.824285402842024),227,Non-crop
+228,228,37.397368184916004,-0.6140394095524093,aasareansah@gmail.com,false,2023-03-07 15:24,12.2 secs,Planet Monthly Mosaics,,POINT(37.397368184916004 -0.614039409552409),228,Crop
+229,229,35.762616150651965,-1.8264944601622872,aasareansah@gmail.com,false,2023-03-07 15:25,9.7 secs,Planet Monthly Mosaics,,POINT(35.762616150651965 -1.826494460162287),229,Non-crop
+230,230,36.54848043845705,-1.000772776636237,acgins@umd.edu,false,2023-03-07 21:12,146.9 secs,Planet Monthly Mosaics,,POINT(36.54848043845705 -1.000772776636237),230,Crop
+231,231,36.02608988189172,3.627360664812145,aasareansah@gmail.com,false,2023-03-07 15:25,17.9 secs,Planet Monthly Mosaics,,POINT(36.02608988189172 3.627360664812145),231,Non-crop
+232,232,33.98152361411099,-0.47111917194585895,aasareansah@gmail.com,false,2023-03-07 15:26,13.4 secs,Planet Monthly Mosaics,,POINT(33.98152361411099 -0.471119171945859),232,Non-crop
+233,233,36.08813405957186,-0.941069865990557,acgins@umd.edu,false,2023-03-07 21:13,60.5 secs,Mapbox Satellite,,POINT(36.08813405957186 -0.941069865990557),233,Crop
+234,234,37.60598532487981,-0.717185806924993,aasareansah@gmail.com,false,2023-03-07 15:26,8.9 secs,Planet Monthly Mosaics,,POINT(37.60598532487981 -0.717185806924993),234,Non-crop
+235,235,34.46747779967232,0.8305382197080086,aasareansah@gmail.com,false,2023-03-07 15:27,39.3 secs,Planet Monthly Mosaics,,POINT(34.46747779967232 0.830538219708009),235,Crop
+236,236,34.840773987663475,-0.36183332301270066,aasareansah@gmail.com,false,2023-03-07 15:27,13.4 secs,Planet Monthly Mosaics,,POINT(34.840773987663475 -0.361833323012701),236,Non-crop
+237,237,37.41092767037453,-2.625588354308176,aasareansah@gmail.com,false,2023-03-07 15:27,8.9 secs,Planet Monthly Mosaics,,POINT(37.41092767037453 -2.625588354308176),237,Non-crop
+238,238,34.5067667042273,4.001073110839722,aasareansah@gmail.com,false,2023-03-07 15:27,9.8 secs,Planet Monthly Mosaics,,POINT(34.5067667042273 4.001073110839722),238,Non-crop
+239,239,37.67487855659351,-0.49020904050426295,acgins@umd.edu,false,2023-03-07 21:13,29.6 secs,Planet Monthly Mosaics,,POINT(37.67487855659351 -0.490209040504263),239,Crop
+240,240,36.81888775078636,1.9849372691074236,aasareansah@gmail.com,false,2023-03-07 15:27,10.9 secs,Planet Monthly Mosaics,,POINT(36.81888775078636 1.984937269107424),240,Non-crop
+241,241,35.24994048248167,-1.0513444774351794,acgins@umd.edu,false,2023-03-07 21:15,114.3 secs,Mapbox Satellite,,POINT(35.24994048248167 -1.051344477435179),241,Crop
+242,242,36.38636671973139,-0.5930179628924186,aasareansah@gmail.com,false,2023-03-07 15:28,12.6 secs,Planet Monthly Mosaics,,POINT(36.38636671973139 -0.593017962892419),242,Non-crop
+243,243,40.907140441399896,0.6303587257539014,aasareansah@gmail.com,false,2023-03-07 15:28,8.3 secs,Planet Monthly Mosaics,,POINT(40.907140441399896 0.630358725753901),243,Non-crop
+244,244,34.96987624938218,1.1655669652004106,aasareansah@gmail.com,false,2023-03-07 15:28,12.3 secs,Planet Monthly Mosaics,,POINT(34.96987624938218 1.165566965200411),244,Crop
+245,245,35.35634367392324,1.4582707365296375,aasareansah@gmail.com,false,2023-03-07 15:28,8.7 secs,Planet Monthly Mosaics,,POINT(35.35634367392324 1.458270736529638),245,Non-crop
+246,246,40.546097997935725,-0.07612710307294997,aasareansah@gmail.com,false,2023-03-07 15:28,8.8 secs,Planet Monthly Mosaics,,POINT(40.546097997935725 -0.07612710307295),246,Non-crop
+247,247,37.16390525162651,-0.7420742630040361,aasareansah@gmail.com,false,2023-03-07 15:28,21.7 secs,Planet Monthly Mosaics,,POINT(37.16390525162651 -0.742074263004036),247,Crop
+248,248,34.45068841495048,0.2338980943932806,aasareansah@gmail.com,false,2023-03-07 15:29,29.1 secs,Planet Monthly Mosaics,,POINT(34.45068841495048 0.233898094393281),248,Crop
+249,249,37.498286295079225,0.9862352131017155,aasareansah@gmail.com,false,2023-03-07 15:29,16.8 secs,Planet Monthly Mosaics,,POINT(37.498286295079225 0.986235213101716),249,Non-crop
+250,250,34.73415295793003,0.018438781875223693,aasareansah@gmail.com,false,2023-03-07 15:29,9.2 secs,Planet Monthly Mosaics,,POINT(34.73415295793003 0.018438781875224),250,Non-crop
+251,251,36.85858969388467,-0.4686485504993926,aasareansah@gmail.com,false,2023-03-07 15:30,19.6 secs,Planet Monthly Mosaics,,POINT(36.85858969388467 -0.468648550499393),251,Crop
+252,252,35.26890826320225,-0.6564145540663221,aasareansah@gmail.com,false,2023-03-07 15:30,34.1 secs,Planet Monthly Mosaics,,POINT(35.26890826320225 -0.656414554066322),252,Crop
+253,253,40.525409290323495,2.6319993375838955,aasareansah@gmail.com,false,2023-03-07 15:30,9.5 secs,Planet Monthly Mosaics,,POINT(40.525409290323495 2.631999337583896),253,Non-crop
+254,254,34.92265893447306,0.5353286427591524,acgins@umd.edu,true,2023-03-07 21:29,19.8 secs,,,POINT(34.92265893447306 0.535328642759152),254,
+255,255,36.90455311386702,1.1977744701246156,aasareansah@gmail.com,false,2023-03-07 15:32,21.4 secs,Planet Monthly Mosaics,,POINT(36.90455311386702 1.197774470124616),255,Non-crop
+256,256,34.5180586260015,0.7789264000463355,aasareansah@gmail.com,false,2023-03-07 15:32,14.3 secs,Planet Monthly Mosaics,,POINT(34.5180586260015 0.778926400046336),256,Non-crop
+257,257,36.058209274389014,-1.3463982887488246,aasareansah@gmail.com,false,2023-03-07 15:32,7.8 secs,Planet Monthly Mosaics,,POINT(36.058209274389014 -1.346398288748825),257,Non-crop
+258,258,39.395468711706776,2.0383453012130155,aasareansah@gmail.com,false,2023-03-07 15:32,8.6 secs,Planet Monthly Mosaics,,POINT(39.395468711706776 2.038345301213016),258,Non-crop
+259,259,39.05025716334389,1.2727642254608016,aasareansah@gmail.com,false,2023-03-07 15:32,11.6 secs,Planet Monthly Mosaics,,POINT(39.05025716334389 1.272764225460802),259,Non-crop
+260,260,40.78878641079977,0.947351946589384,aasareansah@gmail.com,false,2023-03-07 15:33,28.9 secs,Planet Monthly Mosaics,,POINT(40.78878641079977 0.947351946589384),260,Non-crop
+261,261,39.46290672282505,-0.013057674497699916,aasareansah@gmail.com,false,2023-03-07 15:33,25.7 secs,Planet Monthly Mosaics,,POINT(39.46290672282505 -0.0130576744977),261,Non-crop
+262,262,35.19685108842834,3.9040331699588258,aasareansah@gmail.com,false,2023-03-07 15:33,10.8 secs,Planet Monthly Mosaics,,POINT(35.19685108842834 3.904033169958826),262,Non-crop
+263,263,37.299210366889746,-0.7882590697784982,aasareansah@gmail.com,false,2023-03-07 15:33,13.5 secs,Planet Monthly Mosaics,,POINT(37.299210366889746 -0.788259069778498),263,Non-crop
+264,264,35.134539162750926,1.0904367967402915,aasareansah@gmail.com,false,2023-03-07 15:34,23.7 secs,Planet Monthly Mosaics,,POINT(35.134539162750926 1.090436796740292),264,Crop
+265,265,38.19300016102455,-1.6485412210153514,acgins@umd.edu,false,2023-03-07 21:30,42.0 secs,Planet Monthly Mosaics,,POINT(38.19300016102455 -1.648541221015351),265,Crop
+266,266,37.73860020267111,-3.331557513616808,aasareansah@gmail.com,false,2023-03-07 15:35,14.3 secs,Planet Monthly Mosaics,,POINT(37.73860020267111 -3.331557513616808),266,Non-crop
+267,267,36.183154340115976,2.7001405183904446,aasareansah@gmail.com,false,2023-03-07 15:35,9.1 secs,Planet Monthly Mosaics,,POINT(36.183154340115976 2.700140518390445),267,Non-crop
+268,268,39.9003149820409,3.4102972857651417,aasareansah@gmail.com,false,2023-03-07 15:35,8.6 secs,Planet Monthly Mosaics,,POINT(39.9003149820409 3.410297285765142),268,Non-crop
+269,269,36.89256033951802,-1.198551976200557,aasareansah@gmail.com,false,2023-03-07 15:35,15.8 secs,Planet Monthly Mosaics,,POINT(36.89256033951802 -1.198551976200557),269,Non-crop
+270,270,34.111742992464094,-0.03294774771910352,aasareansah@gmail.com,false,2023-03-07 15:35,12.2 secs,Planet Monthly Mosaics,,POINT(34.111742992464094 -0.032947747719104),270,Non-crop
+271,271,38.55271191716017,-1.5870930361786544,aasareansah@gmail.com,false,2023-03-07 15:35,8.5 secs,Planet Monthly Mosaics,,POINT(38.55271191716017 -1.587093036178654),271,Non-crop
+272,272,35.79147363846596,1.8930771879554555,aasareansah@gmail.com,false,2023-03-07 15:35,12.1 secs,Planet Monthly Mosaics,,POINT(35.79147363846596 1.893077187955456),272,Non-crop
+273,273,35.0802486455078,0.7325502982864748,aasareansah@gmail.com,false,2023-03-07 15:36,12.9 secs,Planet Monthly Mosaics,,POINT(35.0802486455078 0.732550298286475),273,Crop
+274,274,36.66787862075995,-0.3370048147857069,aasareansah@gmail.com,false,2023-03-07 15:37,68.4 secs,Planet Monthly Mosaics,,POINT(36.66787862075995 -0.337004814785707),274,Non-crop
+275,275,35.382128847748994,-0.8625910503550166,aasareansah@gmail.com,false,2023-03-07 15:37,40.8 secs,Planet Monthly Mosaics,,POINT(35.382128847748994 -0.862591050355017),275,Crop
+276,276,37.34840090595225,0.23193127521008539,aasareansah@gmail.com,false,2023-03-07 15:38,25.5 secs,Planet Monthly Mosaics,,POINT(37.34840090595225 0.231931275210085),276,Non-crop
+277,277,37.52952524833004,-2.724401129435582,aasareansah@gmail.com,false,2023-03-07 15:39,80.5 secs,Planet Monthly Mosaics,,POINT(37.52952524833004 -2.724401129435582),277,Crop
+278,278,34.30386139976155,0.2951159530038938,aasareansah@gmail.com,false,2023-03-07 15:39,16.3 secs,Mapbox Satellite,,POINT(34.30386139976155 0.295115953003894),278,Non-crop
+279,279,35.664840363056655,1.690372204559844,aasareansah@gmail.com,false,2023-03-07 15:40,24.8 secs,Planet Monthly Mosaics,,POINT(35.664840363056655 1.690372204559844),279,Non-crop
+280,280,39.19739507190026,-1.2216252242734178,aasareansah@gmail.com,false,2023-03-07 15:40,8.0 secs,Planet Monthly Mosaics,,POINT(39.19739507190026 -1.221625224273418),280,Non-crop
+281,281,35.184928340727154,1.8744824920807754,aasareansah@gmail.com,false,2023-03-07 15:40,23.3 secs,Planet Monthly Mosaics,,POINT(35.184928340727154 1.874482492080775),281,Non-crop
+282,282,37.78857700274938,-3.3771286904231905,aasareansah@gmail.com,false,2023-03-07 15:40,9.2 secs,Planet Monthly Mosaics,,POINT(37.78857700274938 -3.37712869042319),282,Non-crop
+283,283,36.68502729602952,2.514325852545885,aasareansah@gmail.com,false,2023-03-07 15:40,10.1 secs,Planet Monthly Mosaics,,POINT(36.68502729602952 2.514325852545885),283,Non-crop
+284,284,35.237040051019214,1.6524385569289506,acgins@umd.edu,false,2023-03-07 21:31,64.2 secs,Planet Monthly Mosaics,,POINT(35.237040051019214 1.652438556928951),284,Crop
+285,285,36.53526300735865,-0.4683613786490718,acgins@umd.edu,false,2023-03-07 21:32,31.9 secs,Planet Monthly Mosaics,,POINT(36.53526300735865 -0.468361378649072),285,Crop
+286,286,38.0760525856689,-0.11913545569567449,aasareansah@gmail.com,false,2023-03-07 15:41,19.8 secs,Planet Monthly Mosaics,,POINT(38.0760525856689 -0.119135455695674),286,Non-crop
+287,287,34.35644028067466,0.5608866070711257,aasareansah@gmail.com,false,2023-03-07 15:42,15.1 secs,Planet Monthly Mosaics,,POINT(34.35644028067466 0.560886607071126),287,Crop
+288,288,38.65484794845974,-0.4860956688299213,aasareansah@gmail.com,false,2023-03-07 15:42,9.7 secs,Planet Monthly Mosaics,,POINT(38.65484794845974 -0.486095668829921),288,Non-crop
+289,289,35.93734608846335,-1.057140931452576,aasareansah@gmail.com,false,2023-03-07 15:42,12.9 secs,Planet Monthly Mosaics,,POINT(35.93734608846335 -1.057140931452576),289,Non-crop
+290,290,37.80582391504641,3.8350499412603556,aasareansah@gmail.com,false,2023-03-07 15:42,9.2 secs,Planet Monthly Mosaics,,POINT(37.80582391504641 3.835049941260356),290,Non-crop
+291,291,35.25737937467433,-0.8666069188032641,aasareansah@gmail.com,false,2023-03-07 15:42,17.7 secs,Planet Monthly Mosaics,,POINT(35.25737937467433 -0.866606918803264),291,Non-crop
+292,292,34.35788383131346,0.19694288433069554,acgins@umd.edu,false,2023-03-07 21:33,78.5 secs,Sentinel-2,,POINT(34.35788383131346 0.196942884330696),292,Crop
+293,293,34.818940898978965,-1.2688823327413155,aasareansah@gmail.com,false,2023-03-07 15:43,13.5 secs,Planet Monthly Mosaics,,POINT(34.818940898978965 -1.268882332741316),293,Non-crop
+294,294,34.93880589858098,2.3229353279690117,aasareansah@gmail.com,false,2023-03-07 15:43,28.6 secs,Planet Monthly Mosaics,,POINT(34.93880589858098 2.322935327969012),294,Non-crop
+295,295,39.054542526331986,0.0026259847551115003,aasareansah@gmail.com,false,2023-03-07 15:43,9.6 secs,Planet Monthly Mosaics,,POINT(39.054542526331986 0.002625984755112),295,Non-crop
+296,296,36.07763478509327,-1.1708826042522547,acgins@umd.edu,false,2023-03-07 21:33,31.7 secs,Planet Monthly Mosaics,,POINT(36.07763478509327 -1.170882604252255),296,Crop
+297,297,34.90669887109912,0.7293370301766702,aasareansah@gmail.com,false,2023-03-07 15:44,30.7 secs,Planet Monthly Mosaics,,POINT(34.90669887109912 0.72933703017667),297,Crop
+298,298,38.79853398079912,0.9642160077813193,aasareansah@gmail.com,false,2023-03-07 15:45,8.8 secs,Planet Monthly Mosaics,,POINT(38.79853398079912 0.964216007781319),298,Non-crop
+299,299,37.553857774558885,-2.9026633735747365,aasareansah@gmail.com,false,2023-03-07 15:45,38.9 secs,Planet Monthly Mosaics,,POINT(37.553857774558885 -2.902663373574736),299,Non-crop
+300,300,38.01347608722678,2.900570657102886,aasareansah@gmail.com,false,2023-03-07 15:45,11.4 secs,Planet Monthly Mosaics,,POINT(38.01347608722678 2.900570657102886),300,Non-crop
+301,301,37.132692772610476,0.39776039039835437,aasareansah@gmail.com,false,2023-03-07 15:45,8.9 secs,Planet Monthly Mosaics,,POINT(37.132692772610476 0.397760390398354),301,Non-crop
+302,302,37.9392642060917,2.9892719733950126,aasareansah@gmail.com,false,2023-03-07 15:46,8.7 secs,Planet Monthly Mosaics,,POINT(37.9392642060917 2.989271973395013),302,Non-crop
+303,303,34.51506919888127,0.1202453794900851,aasareansah@gmail.com,false,2023-03-07 15:46,14.8 secs,Planet Monthly Mosaics,,POINT(34.51506919888127 0.120245379490085),303,Crop
+304,304,39.63767229599631,-0.12049561541535113,aasareansah@gmail.com,false,2023-03-07 15:46,8.8 secs,Planet Monthly Mosaics,,POINT(39.63767229599631 -0.120495615415351),304,Non-crop
+305,305,40.485326212115005,3.093570121762987,aasareansah@gmail.com,false,2023-03-07 15:46,8.2 secs,Planet Monthly Mosaics,,POINT(40.485326212115005 3.093570121762987),305,Non-crop
+306,306,36.85239673523007,-0.05853659594733882,aasareansah@gmail.com,false,2023-03-07 15:46,9.8 secs,Planet Monthly Mosaics,,POINT(36.85239673523007 -0.058536595947339),306,Non-crop
+307,307,37.5763483597854,-0.9823710756791123,aasareansah@gmail.com,false,2023-03-07 15:47,31.9 secs,Planet Monthly Mosaics,,POINT(37.5763483597854 -0.982371075679112),307,Non-crop
+308,308,39.55791935681194,1.5349026436085218,aasareansah@gmail.com,false,2023-03-07 15:47,9.8 secs,Planet Monthly Mosaics,,POINT(39.55791935681194 1.534902643608522),308,Non-crop
+309,309,37.699949750619936,0.811948379073906,aasareansah@gmail.com,false,2023-03-07 15:47,8.5 secs,Planet Monthly Mosaics,,POINT(37.699949750619936 0.811948379073906),309,Non-crop
+310,310,37.61422072618653,-2.0872413462062473,acgins@umd.edu,false,2023-03-07 21:37,230.8 secs,Sentinel-2,,POINT(37.61422072618653 -2.087241346206247),310,Crop
+311,311,41.07513171921359,3.1888676288992936,aasareansah@gmail.com,false,2023-03-07 15:47,14.5 secs,Planet Monthly Mosaics,,POINT(41.07513171921359 3.188867628899294),311,Non-crop
+312,312,38.15815408847578,-0.8111453505853832,aasareansah@gmail.com,false,2023-03-07 15:47,9.0 secs,Planet Monthly Mosaics,,POINT(38.15815408847578 -0.811145350585383),312,Non-crop
+313,313,34.736270179275344,-0.5624127830726717,aasareansah@gmail.com,false,2023-03-07 15:48,19.3 secs,Planet Monthly Mosaics,,POINT(34.736270179275344 -0.562412783072672),313,Crop
+314,314,35.375810528396606,-0.21120699908951038,aasareansah@gmail.com,false,2023-03-07 15:48,13.3 secs,Planet Monthly Mosaics,,POINT(35.375810528396606 -0.21120699908951),314,Crop
+315,315,37.21904951713753,-0.7068700226033653,aasareansah@gmail.com,false,2023-03-07 15:48,11.7 secs,Planet Monthly Mosaics,,POINT(37.21904951713753 -0.706870022603365),315,Non-crop
+316,316,39.83400661063378,-1.68890740390545,aasareansah@gmail.com,false,2023-03-07 15:48,10.5 secs,Planet Monthly Mosaics,,POINT(39.83400661063378 -1.68890740390545),316,Non-crop
+317,317,34.191058880953285,-0.9215307463490227,acgins@umd.edu,false,2023-03-07 21:38,39.2 secs,Planet Monthly Mosaics,,POINT(34.191058880953285 -0.921530746349023),317,Crop
+318,318,34.49184864640101,-0.7460763000252552,acgins@umd.edu,false,2023-03-07 21:39,84.9 secs,Mapbox Satellite,,POINT(34.49184864640101 -0.746076300025255),318,Crop
+319,319,37.651127920351016,-0.6203277536564297,aasareansah@gmail.com,false,2023-03-07 15:49,12.9 secs,Planet Monthly Mosaics,,POINT(37.651127920351016 -0.62032775365643),319,Non-crop
+320,320,38.75780790144454,-0.6667358112308936,aasareansah@gmail.com,false,2023-03-07 15:49,8.3 secs,Planet Monthly Mosaics,,POINT(38.75780790144454 -0.666735811230894),320,Non-crop
+321,321,34.94783457049091,-0.11246745761080304,aasareansah@gmail.com,false,2023-03-07 15:49,12.4 secs,Planet Monthly Mosaics,,POINT(34.94783457049091 -0.112467457610803),321,Non-crop
+322,322,39.229061071429356,-0.666375893413772,aasareansah@gmail.com,false,2023-03-07 15:49,8.0 secs,Planet Monthly Mosaics,,POINT(39.229061071429356 -0.666375893413772),322,Non-crop
+323,323,38.88788044369188,-4.24884778504972,aasareansah@gmail.com,false,2023-03-07 15:49,8.4 secs,Planet Monthly Mosaics,,POINT(38.88788044369188 -4.24884778504972),323,Non-crop
+324,324,36.71627653401663,2.0002891318295455,aasareansah@gmail.com,false,2023-03-07 15:50,9.6 secs,Planet Monthly Mosaics,,POINT(36.71627653401663 2.000289131829546),324,Non-crop
+325,325,35.99051353043517,-1.0041755970886126,aasareansah@gmail.com,false,2023-03-07 15:50,18.8 secs,Planet Monthly Mosaics,,POINT(35.99051353043517 -1.004175597088613),325,Non-crop
+326,326,34.693116889906136,0.7036994275098215,aasareansah@gmail.com,false,2023-03-07 15:50,16.1 secs,Planet Monthly Mosaics,,POINT(34.693116889906136 0.703699427509822),326,Crop
+327,327,36.80110616930354,-1.3991582831341616,aasareansah@gmail.com,false,2023-03-07 15:50,13.0 secs,Planet Monthly Mosaics,,POINT(36.80110616930354 -1.399158283134162),327,Non-crop
+328,328,34.359713263958206,3.7495077872591676,aasareansah@gmail.com,false,2023-03-07 15:50,9.7 secs,Planet Monthly Mosaics,,POINT(34.359713263958206 3.749507787259168),328,Non-crop
+329,329,34.89872798832474,3.7711850926712276,aasareansah@gmail.com,false,2023-03-07 15:50,9.3 secs,Planet Monthly Mosaics,,POINT(34.89872798832474 3.771185092671228),329,Non-crop
+330,330,34.75823814690865,0.768390378516022,acgins@umd.edu,false,2023-03-07 21:41,72.9 secs,Mapbox Satellite,,POINT(34.75823814690865 0.768390378516022),330,Crop
+331,331,35.03043307620979,-0.435833843994421,aasareansah@gmail.com,false,2023-03-07 15:51,25.0 secs,Planet Monthly Mosaics,,POINT(35.03043307620979 -0.435833843994421),331,Crop
+332,332,39.91725992960783,-2.536230581743613,aasareansah@gmail.com,false,2023-03-07 15:51,12.6 secs,Planet Monthly Mosaics,,POINT(39.91725992960783 -2.536230581743613),332,Non-crop
+333,333,38.04110617648035,2.134810668583166,aasareansah@gmail.com,false,2023-03-07 15:51,9.8 secs,Planet Monthly Mosaics,,POINT(38.04110617648035 2.134810668583166),333,Non-crop
+334,334,38.616261925366786,2.5167846397007954,aasareansah@gmail.com,false,2023-03-07 15:51,8.8 secs,Planet Monthly Mosaics,,POINT(38.616261925366786 2.516784639700795),334,Non-crop
+335,335,36.910665570255915,-1.1618142066887174,aasareansah@gmail.com,false,2023-03-07 15:52,11.6 secs,Planet Monthly Mosaics,,POINT(36.910665570255915 -1.161814206688717),335,Non-crop
+336,336,36.42768875867901,2.649630024893417,aasareansah@gmail.com,false,2023-03-07 15:52,10.0 secs,Planet Monthly Mosaics,,POINT(36.42768875867901 2.649630024893417),336,Non-crop
+337,337,37.41839115014418,-1.0816838435200997,aasareansah@gmail.com,false,2023-03-07 15:52,36.6 secs,Planet Monthly Mosaics,,POINT(37.41839115014418 -1.0816838435201),337,Non-crop
+338,338,37.10693932494706,-0.6350245887261098,aasareansah@gmail.com,false,2023-03-07 15:52,18.5 secs,Planet Monthly Mosaics,,POINT(37.10693932494706 -0.63502458872611),338,Non-crop
+339,339,36.984429661995755,3.311675194970429,aasareansah@gmail.com,false,2023-03-07 15:53,8.4 secs,Planet Monthly Mosaics,,POINT(36.984429661995755 3.311675194970429),339,Non-crop
+340,340,36.144412583364996,0.004963373055491912,acgins@umd.edu,false,2023-03-07 21:44,214.8 secs,Planet Monthly Mosaics,,POINT(36.144412583364996 0.004963373055492),340,Crop
+341,341,36.70078650447418,3.309703935432483,aasareansah@gmail.com,false,2023-03-07 15:53,8.4 secs,Planet Monthly Mosaics,,POINT(36.70078650447418 3.309703935432483),341,Non-crop
+342,342,38.75161279448816,2.378729348687549,aasareansah@gmail.com,false,2023-03-07 15:53,8.3 secs,Planet Monthly Mosaics,,POINT(38.75161279448816 2.378729348687549),342,Non-crop
+343,343,40.06707636849588,3.45275268077823,aasareansah@gmail.com,false,2023-03-07 15:53,12.2 secs,Planet Monthly Mosaics,,POINT(40.06707636849588 3.45275268077823),343,Non-crop
+344,344,36.56210500652624,-1.0837246415363992,aasareansah@gmail.com,false,2023-03-07 15:53,8.7 secs,Planet Monthly Mosaics,,POINT(36.56210500652624 -1.083724641536399),344,Non-crop
+345,345,34.336041577902456,0.7166491969812425,acgins@umd.edu,false,2023-03-07 21:46,139.9 secs,Mapbox Satellite,,POINT(34.336041577902456 0.716649196981242),345,Crop
+346,346,39.598853154828326,2.8858817878871452,aasareansah@gmail.com,false,2023-03-07 15:54,9.1 secs,Planet Monthly Mosaics,,POINT(39.598853154828326 2.885881787887145),346,Non-crop
+347,347,36.43918846011223,1.440504544935712,aasareansah@gmail.com,false,2023-03-07 15:54,8.6 secs,Planet Monthly Mosaics,,POINT(36.43918846011223 1.440504544935712),347,Non-crop
+348,348,35.54210939603061,-1.327991441743508,aasareansah@gmail.com,false,2023-03-07 15:54,8.3 secs,Planet Monthly Mosaics,,POINT(35.54210939603061 -1.327991441743508),348,Non-crop
+349,349,34.24661018198986,3.7788091335579623,aasareansah@gmail.com,false,2023-03-07 15:54,8.4 secs,Planet Monthly Mosaics,,POINT(34.24661018198986 3.778809133557962),349,Non-crop
+350,350,38.155177833382425,-0.9094747900561307,aasareansah@gmail.com,false,2023-03-07 15:54,16.8 secs,Planet Monthly Mosaics,,POINT(38.155177833382425 -0.909474790056131),350,Non-crop
+351,351,37.72392326099627,-2.8587133707172647,aasareansah@gmail.com,false,2023-03-07 15:54,8.7 secs,Planet Monthly Mosaics,,POINT(37.72392326099627 -2.858713370717265),351,Non-crop
+352,352,39.185711371300165,0.11214360263653626,aasareansah@gmail.com,false,2023-03-07 15:54,8.1 secs,Planet Monthly Mosaics,,POINT(39.185711371300165 0.112143602636536),352,Non-crop
+353,353,36.81517663408579,0.06603508799998077,aasareansah@gmail.com,false,2023-03-07 15:54,9.3 secs,Planet Monthly Mosaics,,POINT(36.81517663408579 0.066035087999981),353,Non-crop
+354,354,39.73857946305931,1.3005086068260963,aasareansah@gmail.com,false,2023-03-07 15:55,8.1 secs,Planet Monthly Mosaics,,POINT(39.73857946305931 1.300508606826096),354,Non-crop
+355,355,34.89094893452659,0.5432189512967784,aasareansah@gmail.com,false,2023-03-07 15:55,29.7 secs,Planet Monthly Mosaics,,POINT(34.89094893452659 0.543218951296778),355,Crop
+356,356,35.34730497147133,1.8974096389644381,aasareansah@gmail.com,false,2023-03-07 15:55,13.1 secs,Planet Monthly Mosaics,,POINT(35.34730497147133 1.897409638964438),356,Non-crop
+357,357,39.4560023573566,-3.7589481349304537,aasareansah@gmail.com,false,2023-03-07 15:55,13.6 secs,Planet Monthly Mosaics,,POINT(39.4560023573566 -3.758948134930454),357,Non-crop
+358,358,34.19309376608378,-0.7112667390382308,acgins@umd.edu,false,2023-03-07 21:48,87.5 secs,Planet Monthly Mosaics,,POINT(34.19309376608378 -0.711266739038231),358,Crop
+359,359,38.08520562034121,-0.7104578632257538,aasareansah@gmail.com,false,2023-03-07 15:56,24.5 secs,Planet Monthly Mosaics,,POINT(38.08520562034121 -0.710457863225754),359,Non-crop
+360,360,39.8764623690857,-0.41394507470955105,acgins@umd.edu,false,2023-03-07 21:49,65.4 secs,Mapbox Satellite,,POINT(39.8764623690857 -0.413945074709551),360,Non-crop
+361,361,36.59090129950348,4.401266892103294,aasareansah@gmail.com,false,2023-03-07 15:57,26.6 secs,Planet Monthly Mosaics,,POINT(36.59090129950348 4.401266892103294),361,Non-crop
+362,362,36.80582306681952,3.5421413369810377,aasareansah@gmail.com,false,2023-03-07 15:57,10.2 secs,Planet Monthly Mosaics,,POINT(36.80582306681952 3.542141336981038),362,Non-crop
+363,363,40.84664060103932,1.7168775775104577,aasareansah@gmail.com,false,2023-03-07 15:57,11.0 secs,Planet Monthly Mosaics,,POINT(40.84664060103932 1.716877577510458),363,Non-crop
+364,364,40.68156500663407,1.129225977950579,aasareansah@gmail.com,false,2023-03-07 15:57,15.2 secs,Planet Monthly Mosaics,,POINT(40.68156500663407 1.129225977950579),364,Non-crop
+365,365,38.55642953523764,2.918040794846058,aasareansah@gmail.com,false,2023-03-07 15:57,10.1 secs,Planet Monthly Mosaics,,POINT(38.55642953523764 2.918040794846058),365,Non-crop
+366,366,39.0709024919474,-3.7913328870442045,aasareansah@gmail.com,false,2023-03-07 15:57,12.4 secs,Planet Monthly Mosaics,,POINT(39.0709024919474 -3.791332887044204),366,Non-crop
+367,367,36.638130724991306,-0.49460489559864695,aasareansah@gmail.com,false,2023-03-07 15:58,12.2 secs,Planet Monthly Mosaics,,POINT(36.638130724991306 -0.494604895598647),367,Non-crop
+368,368,37.63079766174867,-0.053604990844186254,aasareansah@gmail.com,false,2023-03-07 15:58,22.7 secs,Planet Monthly Mosaics,,POINT(37.63079766174867 -0.053604990844186),368,Crop
+369,369,35.827907745167956,-0.853811982262311,aasareansah@gmail.com,false,2023-03-07 15:58,8.6 secs,Planet Monthly Mosaics,,POINT(35.827907745167956 -0.853811982262311),369,Non-crop
+370,370,39.167720267265224,1.9374319528339419,aasareansah@gmail.com,false,2023-03-07 15:58,12.7 secs,Planet Monthly Mosaics,,POINT(39.167720267265224 1.937431952833942),370,Non-crop
+371,371,34.212762133967715,-0.8357804134194107,acgins@umd.edu,false,2023-03-07 21:51,101.5 secs,Planet Monthly Mosaics,,POINT(34.212762133967715 -0.835780413419411),371,Crop
+372,372,37.96110516185624,0.14781455235668403,aasareansah@gmail.com,false,2023-03-07 15:59,13.9 secs,Planet Monthly Mosaics,,POINT(37.96110516185624 0.147814552356684),372,Non-crop
+373,373,34.47463753982868,0.10990526138305981,acgins@umd.edu,false,2023-03-07 21:54,218.7 secs,Planet Monthly Mosaics,,POINT(34.47463753982868 0.10990526138306),373,Non-crop
+374,374,39.69251931795742,-2.5541267260917877,aasareansah@gmail.com,false,2023-03-07 15:59,11.7 secs,Planet Monthly Mosaics,,POINT(39.69251931795742 -2.554126726091788),374,Non-crop
+375,375,36.68244024793016,-1.8676442073681212,aasareansah@gmail.com,false,2023-03-07 15:59,8.5 secs,Planet Monthly Mosaics,,POINT(36.68244024793016 -1.867644207368121),375,Non-crop
+376,376,36.853159792048174,3.4206556695885375,aasareansah@gmail.com,false,2023-03-07 15:59,9.1 secs,Planet Monthly Mosaics,,POINT(36.853159792048174 3.420655669588538),376,Non-crop
+377,377,38.92586119879842,1.3194550813766501,aasareansah@gmail.com,false,2023-03-07 16:00,12.7 secs,Planet Monthly Mosaics,,POINT(38.92586119879842 1.31945508137665),377,Non-crop
+378,378,38.69999902903868,2.7545836053240813,aasareansah@gmail.com,false,2023-03-07 16:00,8.9 secs,Planet Monthly Mosaics,,POINT(38.69999902903868 2.754583605324081),378,Non-crop
+379,379,34.83181317134586,1.144867592720978,aasareansah@gmail.com,false,2023-03-07 16:00,12.2 secs,Planet Monthly Mosaics,,POINT(34.83181317134586 1.144867592720978),379,Crop
+380,380,40.90693313748294,1.8439313546702625,aasareansah@gmail.com,false,2023-03-07 16:00,8.2 secs,Planet Monthly Mosaics,,POINT(40.90693313748294 1.843931354670262),380,Non-crop
+381,381,34.1304218109236,-0.5631023328537231,aasareansah@gmail.com,false,2023-03-07 16:00,9.0 secs,Planet Monthly Mosaics,,POINT(34.1304218109236 -0.563102332853723),381,Non-crop
+382,382,39.200957664948234,-2.5959413740347235,aasareansah@gmail.com,false,2023-03-07 16:00,11.6 secs,Planet Monthly Mosaics,,POINT(39.200957664948234 -2.595941374034724),382,Non-crop
+383,383,38.63760081316255,1.1224819906657701,aasareansah@gmail.com,false,2023-03-07 16:00,7.6 secs,Planet Monthly Mosaics,,POINT(38.63760081316255 1.12248199066577),383,Non-crop
+384,384,34.795026265560566,0.6743079317838352,acgins@umd.edu,false,2023-03-07 21:55,61.5 secs,Mapbox Satellite,,POINT(34.795026265560566 0.674307931783835),384,Crop
+385,385,40.272953061237764,-1.7216502223117192,aasareansah@gmail.com,false,2023-03-07 16:01,8.9 secs,Planet Monthly Mosaics,,POINT(40.272953061237764 -1.721650222311719),385,Non-crop
+386,386,34.511216655298945,-0.40840217196105755,aasareansah@gmail.com,false,2023-03-07 16:01,8.5 secs,Planet Monthly Mosaics,,POINT(34.511216655298945 -0.408402171961058),386,Non-crop
+387,387,38.532332486665574,-3.4722325425673475,aasareansah@gmail.com,false,2023-03-07 16:01,7.3 secs,Planet Monthly Mosaics,,POINT(38.532332486665574 -3.472232542567348),387,Non-crop
+388,388,34.824113704860466,1.2169539748385623,aasareansah@gmail.com,false,2023-03-07 16:01,12.7 secs,Planet Monthly Mosaics,,POINT(34.824113704860466 1.216953974838562),388,Crop
+389,389,37.388102475893305,-0.7540557617057277,aasareansah@gmail.com,false,2023-03-07 16:02,29.1 secs,Planet Monthly Mosaics,,POINT(37.388102475893305 -0.754055761705728),389,Non-crop
+390,390,37.42891460399352,-0.45483461285550036,acgins@umd.edu,false,2023-03-07 21:58,144.0 secs,Mapbox Satellite,,POINT(37.42891460399352 -0.4548346128555),390,Crop
+391,391,36.967887534726245,-0.8095488907087274,aasareansah@gmail.com,false,2023-03-07 16:02,19.7 secs,Planet Monthly Mosaics,,POINT(36.967887534726245 -0.809548890708727),391,Non-crop
+392,392,35.527048399962815,4.0989025202231,aasareansah@gmail.com,false,2023-03-07 16:02,10.0 secs,Planet Monthly Mosaics,,POINT(35.527048399962815 4.0989025202231),392,Non-crop
+393,393,36.924922755405284,-1.1232711340297845,aasareansah@gmail.com,false,2023-03-07 16:02,9.2 secs,Planet Monthly Mosaics,,POINT(36.924922755405284 -1.123271134029784),393,Non-crop
+394,394,37.257050079664545,0.7989869091198432,aasareansah@gmail.com,false,2023-03-07 16:02,8.5 secs,Planet Monthly Mosaics,,POINT(37.257050079664545 0.798986909119843),394,Non-crop
+395,395,38.95454874266865,3.252171003985754,aasareansah@gmail.com,false,2023-03-07 16:02,9.1 secs,Planet Monthly Mosaics,,POINT(38.95454874266865 3.252171003985754),395,Non-crop
+396,396,35.341565025437944,-0.29359632555640297,acgins@umd.edu,true,2023-03-07 22:04,397.8 secs,,,POINT(35.341565025437944 -0.293596325556403),396,
+397,397,35.79074795959633,-1.9464192254714852,aasareansah@gmail.com,false,2023-03-07 16:03,12.1 secs,Planet Monthly Mosaics,,POINT(35.79074795959633 -1.946419225471485),397,Non-crop
+398,398,34.651917881792194,0.5319953940694888,aasareansah@gmail.com,false,2023-03-07 16:03,8.1 secs,Planet Monthly Mosaics,,POINT(34.651917881792194 0.531995394069489),398,Non-crop
+399,399,41.1306620114978,3.1745220367098654,aasareansah@gmail.com,false,2023-03-07 16:03,7.8 secs,Planet Monthly Mosaics,,POINT(41.1306620114978 3.174522036709865),399,Non-crop
+400,400,34.4371951436428,-0.7709979914980767,aasareansah@gmail.com,false,2023-03-07 16:03,18.7 secs,Planet Monthly Mosaics,,POINT(34.4371951436428 -0.770997991498077),400,Crop
+401,401,34.558043775685306,3.125264995688962,aasareansah@gmail.com,false,2023-03-07 16:03,9.4 secs,Planet Monthly Mosaics,,POINT(34.558043775685306 3.125264995688962),401,Non-crop
+402,402,37.981040475396064,3.6028567914524987,aasareansah@gmail.com,false,2023-03-07 16:04,22.9 secs,Planet Monthly Mosaics,,POINT(37.981040475396064 3.602856791452499),402,Non-crop
+403,403,38.14269055562707,0.6772356097837042,aasareansah@gmail.com,false,2023-03-07 16:04,7.9 secs,Planet Monthly Mosaics,,POINT(38.14269055562707 0.677235609783704),403,Non-crop
+404,404,34.46795120792515,0.6249513233906141,acgins@umd.edu,false,2023-03-07 22:07,139.8 secs,Sentinel-2,,POINT(34.46795120792515 0.624951323390614),404,Crop
+405,405,35.18905016180328,-0.9303939864286976,aasareansah@gmail.com,false,2023-03-07 16:05,41.3 secs,Planet Monthly Mosaics,,POINT(35.18905016180328 -0.930393986428698),405,Crop
+406,406,34.680031744225815,-1.056354575251415,acgins@umd.edu,false,2023-03-07 22:10,189.6 secs,Mapbox Satellite,,POINT(34.680031744225815 -1.056354575251415),406,Non-crop
+407,407,38.584107662616205,-4.000071152288873,aasareansah@gmail.com,false,2023-03-07 16:05,10.0 secs,Planet Monthly Mosaics,,POINT(38.584107662616205 -4.000071152288873),407,Non-crop
+408,408,34.54922635792607,0.568676232467724,aasareansah@gmail.com,false,2023-03-07 16:05,12.9 secs,Planet Monthly Mosaics,,POINT(34.54922635792607 0.568676232467724),408,Non-crop
+409,409,34.46976620330974,0.5891906542491239,acgins@umd.edu,false,2023-03-07 22:11,43.5 secs,Mapbox Satellite,,POINT(34.46976620330974 0.589190654249124),409,Crop
+410,410,34.317026783547334,-0.14004161925952469,aasareansah@gmail.com,false,2023-03-07 16:06,24.2 secs,Planet Monthly Mosaics,,POINT(34.317026783547334 -0.140041619259525),410,Crop
+411,411,37.67958111982131,-0.031077482460660336,aasareansah@gmail.com,false,2023-03-07 16:06,12.3 secs,Planet Monthly Mosaics,,POINT(37.67958111982131 -0.03107748246066),411,Non-crop
+412,412,34.97698104242654,0.5048837047733515,acgins@umd.edu,false,2023-03-07 22:13,114.7 secs,Planet Monthly Mosaics,,POINT(34.97698104242654 0.504883704773352),412,Crop
+413,413,39.42808479465304,2.647284919989776,aasareansah@gmail.com,false,2023-03-07 16:06,11.4 secs,Planet Monthly Mosaics,,POINT(39.42808479465304 2.647284919989776),413,Non-crop
+414,414,36.941839061604995,3.049551867540078,aasareansah@gmail.com,false,2023-03-07 16:06,7.8 secs,Planet Monthly Mosaics,,POINT(36.941839061604995 3.049551867540078),414,Non-crop
+415,415,34.79602405819177,0.09970469632021167,aasareansah@gmail.com,false,2023-03-07 16:06,21.4 secs,Planet Monthly Mosaics,,POINT(34.79602405819177 0.099704696320212),415,Non-crop
+416,416,34.566268622694864,-0.9309187229149422,aasareansah@gmail.com,false,2023-03-07 16:07,23.7 secs,Planet Monthly Mosaics,,POINT(34.566268622694864 -0.930918722914942),416,Crop
+417,417,34.193493927332696,-0.7542905932579258,aasareansah@gmail.com,false,2023-03-07 16:07,15.2 secs,Planet Monthly Mosaics,,POINT(34.193493927332696 -0.754290593257926),417,Crop
+418,418,37.15096183503388,-0.33350560480048325,acgins@umd.edu,false,2023-03-07 22:14,63.1 secs,Mapbox Satellite,,POINT(37.15096183503388 -0.333505604800483),418,Crop
+419,419,34.51308490339396,-0.7547247822555792,acgins@umd.edu,false,2023-03-08 01:25,11512.2 secs,Mapbox Satellite,,POINT(34.51308490339396 -0.754724782255579),419,Non-crop
+420,420,36.50791814531247,-0.7870475476290038,adadebay@umd.edu,false,2023-03-08 17:58,28.5 secs,Mapbox Satellite,,POINT(36.50791814531247 -0.787047547629004),420,Crop
+421,421,37.82868384399238,3.714236460960642,aasareansah@gmail.com,false,2023-03-07 16:08,9.2 secs,Planet Monthly Mosaics,,POINT(37.82868384399238 3.714236460960642),421,Non-crop
+422,422,40.009040766686724,-0.3419198637588498,aasareansah@gmail.com,false,2023-03-07 16:08,9.5 secs,Planet Monthly Mosaics,,POINT(40.009040766686724 -0.34191986375885),422,Non-crop
+423,423,34.491276416658955,-1.2346310142928727,adadebay@umd.edu,false,2023-03-08 17:58,11.5 secs,Mapbox Satellite,,POINT(34.491276416658955 -1.234631014292873),423,Crop
+424,424,35.15441964409439,-0.43733764965412875,aasareansah@gmail.com,false,2023-03-07 16:12,161.7 secs,Mapbox Satellite,,POINT(35.15441964409439 -0.437337649654129),424,Non-crop
+425,425,35.28686492785766,4.213669588373389,aasareansah@gmail.com,false,2023-03-07 16:13,62.7 secs,Planet Monthly Mosaics,,POINT(35.28686492785766 4.213669588373389),425,Non-crop
+426,426,40.499705577963645,3.056516667636345,aasareansah@gmail.com,false,2023-03-07 16:13,10.6 secs,Planet Monthly Mosaics,,POINT(40.499705577963645 3.056516667636345),426,Non-crop
+427,427,40.30464352437439,-0.4326296899359431,aasareansah@gmail.com,false,2023-03-07 16:13,10.4 secs,Planet Monthly Mosaics,,POINT(40.30464352437439 -0.432629689935943),427,Non-crop
+428,428,39.34917441922158,1.099228413243411,aasareansah@gmail.com,false,2023-03-07 16:13,8.1 secs,Planet Monthly Mosaics,,POINT(39.34917441922158 1.099228413243411),428,Non-crop
+429,429,34.26078466118539,-1.0247476972595801,adadebay@umd.edu,false,2023-03-08 17:59,10.5 secs,Mapbox Satellite,,POINT(34.26078466118539 -1.02474769725958),429,Crop
+430,430,39.74078659873234,-1.1870999208832935,aasareansah@gmail.com,false,2023-03-07 16:13,8.1 secs,Planet Monthly Mosaics,,POINT(39.74078659873234 -1.187099920883294),430,Non-crop
+431,431,34.76303145495171,0.4669028149604851,aasareansah@gmail.com,false,2023-03-07 16:14,34.1 secs,Planet Monthly Mosaics,,POINT(34.76303145495171 0.466902814960485),431,Crop
+432,432,37.39470110843973,-0.5501557583050474,aasareansah@gmail.com,false,2023-03-07 16:14,17.3 secs,Planet Monthly Mosaics,,POINT(37.39470110843973 -0.550155758305047),432,Non-crop
+433,433,37.174734166271435,-2.02859100605471,aasareansah@gmail.com,false,2023-03-07 16:14,9.2 secs,Planet Monthly Mosaics,,POINT(37.174734166271435 -2.02859100605471),433,Non-crop
+434,434,35.7713835612932,1.7742478484709758,aasareansah@gmail.com,false,2023-03-07 16:14,11.7 secs,Planet Monthly Mosaics,,POINT(35.7713835612932 1.774247848470976),434,Non-crop
+435,435,35.054458324171065,-0.44070243296815076,aasareansah@gmail.com,false,2023-03-07 16:14,17.2 secs,Planet Monthly Mosaics,,POINT(35.054458324171065 -0.440702432968151),435,Crop
+436,436,35.535317107443134,4.0300216964176565,aasareansah@gmail.com,false,2023-03-07 16:15,9.3 secs,Planet Monthly Mosaics,,POINT(35.535317107443134 4.030021696417656),436,Non-crop
+437,437,36.195615641572736,1.2500737632246104,aasareansah@gmail.com,false,2023-03-07 16:15,8.9 secs,Planet Monthly Mosaics,,POINT(36.195615641572736 1.25007376322461),437,Non-crop
+438,438,34.585500564173465,-1.1006159436375704,aasareansah@gmail.com,false,2023-03-07 16:15,23.8 secs,Planet Monthly Mosaics,,POINT(34.585500564173465 -1.10061594363757),438,Crop
+439,439,36.93681116460859,-0.8568247999425626,aasareansah@gmail.com,false,2023-03-07 16:15,7.5 secs,Planet Monthly Mosaics,,POINT(36.93681116460859 -0.856824799942563),439,Non-crop
+440,440,34.653940619977,-1.2979980707441048,aasareansah@gmail.com,false,2023-03-07 16:15,8.3 secs,Planet Monthly Mosaics,,POINT(34.653940619977 -1.297998070744105),440,Non-crop
+441,441,39.95325288909631,-3.0539655940358257,aasareansah@gmail.com,false,2023-03-07 16:15,10.1 secs,Planet Monthly Mosaics,,POINT(39.95325288909631 -3.053965594035826),441,Non-crop
+442,442,34.79643269660636,0.954060045767657,adadebay@umd.edu,false,2023-03-08 17:59,44.1 secs,Mapbox Satellite,,POINT(34.79643269660636 0.954060045767657),442,Non-crop
+443,443,37.64560569646061,-0.4995748989339322,adadebay@umd.edu,false,2023-03-08 17:59,13.2 secs,Mapbox Satellite,,POINT(37.64560569646061 -0.499574898933932),443,Crop
+444,444,35.193697555577735,-1.0090949072315327,aasareansah@gmail.com,false,2023-03-07 16:16,37.0 secs,Planet Monthly Mosaics,,POINT(35.193697555577735 -1.009094907231533),444,Non-crop
+445,445,34.29444546033131,-0.1648308472018498,adadebay@umd.edu,false,2023-03-08 18:00,24.5 secs,Mapbox Satellite,,POINT(34.29444546033131 -0.16483084720185),445,Crop
+446,446,34.314974037204585,-0.8050913043294229,isha9a@umd.edu,false,2023-03-08 21:03,61.4 secs,Mapbox Satellite,,POINT(34.314974037204585 -0.805091304329423),446,Non-crop
+447,447,40.31294294731426,-1.7926326213420247,aasareansah@gmail.com,false,2023-03-07 16:17,13.0 secs,Planet Monthly Mosaics,,POINT(40.31294294731426 -1.792632621342025),447,Non-crop
+448,448,36.91616303309522,-2.5727932209337974,aasareansah@gmail.com,false,2023-03-07 16:17,8.0 secs,Planet Monthly Mosaics,,POINT(36.91616303309522 -2.572793220933797),448,Non-crop
+449,449,40.3485941388307,-1.8043211728138038,aasareansah@gmail.com,false,2023-03-07 16:18,63.2 secs,Planet Monthly Mosaics,,POINT(40.3485941388307 -1.804321172813804),449,Non-crop
+450,450,34.75482976822808,1.2027323213729808,aasareansah@gmail.com,false,2023-03-07 16:18,24.0 secs,Planet Monthly Mosaics,,POINT(34.75482976822808 1.202732321372981),450,Crop
+451,451,35.039639014761306,4.2483244000980145,aasareansah@gmail.com,false,2023-03-07 16:19,7.9 secs,Planet Monthly Mosaics,,POINT(35.039639014761306 4.248324400098014),451,Non-crop
+452,452,40.2874788432159,2.504938312987063,aasareansah@gmail.com,false,2023-03-07 16:19,8.0 secs,Planet Monthly Mosaics,,POINT(40.2874788432159 2.504938312987063),452,Non-crop
+453,453,37.573692021019895,-0.004673769817114266,aasareansah@gmail.com,false,2023-03-07 16:19,8.2 secs,Planet Monthly Mosaics,,POINT(37.573692021019895 -0.004673769817114),453,Non-crop
+454,454,36.807037684141676,2.542819879774906,aasareansah@gmail.com,false,2023-03-07 16:19,10.9 secs,Planet Monthly Mosaics,,POINT(36.807037684141676 2.542819879774906),454,Non-crop
+455,455,37.0048015872321,-0.45869277751116916,aasareansah@gmail.com,false,2023-03-07 16:19,16.3 secs,Planet Monthly Mosaics,,POINT(37.0048015872321 -0.458692777511169),455,Non-crop
+456,456,36.84196187497421,3.7249805816569435,aasareansah@gmail.com,false,2023-03-07 16:19,8.3 secs,Planet Monthly Mosaics,,POINT(36.84196187497421 3.724980581656944),456,Non-crop
+457,457,34.649646702806294,0.6912557299441862,isha9a@umd.edu,false,2023-03-08 21:07,203.2 secs,Mapbox Satellite,,POINT(34.649646702806294 0.691255729944186),457,Non-crop
+458,458,39.27351047995854,2.382870821022328,aasareansah@gmail.com,false,2023-03-07 16:19,12.2 secs,Planet Monthly Mosaics,,POINT(39.27351047995854 2.382870821022328),458,Non-crop
+459,459,34.09255046019023,0.13750458019816195,isha9a@umd.edu,false,2023-03-08 21:10,239.0 secs,Mapbox Satellite,,POINT(34.09255046019023 0.137504580198162),459,Non-crop
+460,460,35.196367961673985,-0.9384964043103956,aasareansah@gmail.com,false,2023-03-07 16:20,12.1 secs,Planet Monthly Mosaics,,POINT(35.196367961673985 -0.938496404310396),460,Non-crop
+461,461,34.729242476157864,-0.006280115060495967,isha9a@umd.edu,false,2023-03-08 21:16,331.4 secs,Planet Monthly Mosaics,,POINT(34.729242476157864 -0.006280115060496),461,Crop
+462,462,37.14320247144663,-0.5799924116994661,aasareansah@gmail.com,false,2023-03-07 16:20,13.2 secs,Planet Monthly Mosaics,,POINT(37.14320247144663 -0.579992411699466),462,Non-crop
+463,463,34.839386313724425,-0.5907161983375747,aasareansah@gmail.com,false,2023-03-07 16:20,14.5 secs,Planet Monthly Mosaics,,POINT(34.839386313724425 -0.590716198337575),463,Crop
+464,464,37.298134606247274,-2.7530158736682773,aasareansah@gmail.com,false,2023-03-07 16:21,8.9 secs,Planet Monthly Mosaics,,POINT(37.298134606247274 -2.753015873668277),464,Non-crop
+465,465,37.235080182010115,3.2939567840656823,aasareansah@gmail.com,false,2023-03-07 16:21,11.8 secs,Planet Monthly Mosaics,,POINT(37.235080182010115 3.293956784065682),465,Non-crop
+466,466,34.620563644213945,3.1399987547652173,aasareansah@gmail.com,false,2023-03-07 16:21,9.6 secs,Planet Monthly Mosaics,,POINT(34.620563644213945 3.139998754765217),466,Non-crop
+467,467,37.90330112445743,2.0453183232955094,aasareansah@gmail.com,false,2023-03-07 16:21,9.5 secs,Planet Monthly Mosaics,,POINT(37.90330112445743 2.045318323295509),467,Non-crop
+468,468,38.75219866912606,1.4087696247600232,aasareansah@gmail.com,false,2023-03-07 16:21,8.3 secs,Planet Monthly Mosaics,,POINT(38.75219866912606 1.408769624760023),468,Non-crop
+469,469,36.09478312311987,-0.45270767372427245,aasareansah@gmail.com,false,2023-03-07 16:21,13.3 secs,Planet Monthly Mosaics,,POINT(36.09478312311987 -0.452707673724272),469,Non-crop
+470,470,39.14544637042974,3.0526918285416156,aasareansah@gmail.com,false,2023-03-07 16:21,9.1 secs,Planet Monthly Mosaics,,POINT(39.14544637042974 3.052691828541616),470,Non-crop
+471,471,36.986050045933645,0.8166083567094135,aasareansah@gmail.com,false,2023-03-07 16:21,11.7 secs,Planet Monthly Mosaics,,POINT(36.986050045933645 0.816608356709414),471,Non-crop
+472,472,36.67508229636412,2.360931840234958,aasareansah@gmail.com,false,2023-03-07 16:22,8.3 secs,Planet Monthly Mosaics,,POINT(36.67508229636412 2.360931840234958),472,Non-crop
+473,473,34.87929322199816,-1.3410826721831222,aasareansah@gmail.com,false,2023-03-07 16:22,7.9 secs,Planet Monthly Mosaics,,POINT(34.87929322199816 -1.341082672183122),473,Non-crop
+474,474,36.96034269804192,0.6752585022986302,aasareansah@gmail.com,false,2023-03-07 16:22,9.5 secs,Planet Monthly Mosaics,,POINT(36.96034269804192 0.67525850229863),474,Non-crop
+475,475,38.01094738788046,0.4034110699662577,aasareansah@gmail.com,false,2023-03-07 16:22,11.6 secs,Planet Monthly Mosaics,,POINT(38.01094738788046 0.403411069966258),475,Non-crop
+476,476,37.678195029831784,0.4669841625774332,aasareansah@gmail.com,false,2023-03-07 16:22,11.0 secs,Planet Monthly Mosaics,,POINT(37.678195029831784 0.466984162577433),476,Non-crop
+477,477,35.56489986263098,4.17275389765291,aasareansah@gmail.com,false,2023-03-07 16:22,8.1 secs,Planet Monthly Mosaics,,POINT(35.56489986263098 4.17275389765291),477,Non-crop
+478,478,38.89991542855711,-0.10599067675841689,aasareansah@gmail.com,false,2023-03-07 16:22,8.6 secs,Planet Monthly Mosaics,,POINT(38.89991542855711 -0.105990676758417),478,Non-crop
+479,479,38.16088744929678,-0.5340005076082127,isha9a@umd.edu,false,2023-03-08 21:19,180.5 secs,Planet Monthly Mosaics,,POINT(38.16088744929678 -0.534000507608213),479,Crop
+480,480,36.31334231145933,1.7564825870660954,aasareansah@gmail.com,false,2023-03-07 16:22,8.4 secs,Planet Monthly Mosaics,,POINT(36.31334231145933 1.756482587066095),480,Non-crop
+481,481,36.05446021526722,-0.7701864509339067,isha9a@umd.edu,false,2023-03-08 21:24,327.0 secs,Planet Monthly Mosaics,,POINT(36.05446021526722 -0.770186450933907),481,Non-crop
+482,482,34.634601312187485,0.08943773979142051,isha9a@umd.edu,false,2023-03-08 21:28,228.9 secs,Mapbox Satellite,,POINT(34.634601312187485 0.089437739791421),482,Non-crop
+483,483,37.91044452150111,3.5311304324163824,aasareansah@gmail.com,false,2023-03-07 16:23,10.6 secs,Planet Monthly Mosaics,,POINT(37.91044452150111 3.531130432416382),483,Non-crop
+484,484,34.5575298635729,-1.2831810660561516,isha9a@umd.edu,false,2023-03-08 21:29,71.7 secs,Mapbox Satellite,,POINT(34.5575298635729 -1.283181066056152),484,Crop
+485,485,35.39516029245672,-0.8794258046257776,isha9a@umd.edu,false,2023-03-08 21:32,159.1 secs,Planet Monthly Mosaics,,POINT(35.39516029245672 -0.879425804625778),485,Non-crop
+486,486,38.321928188337154,3.043383821526915,aasareansah@gmail.com,false,2023-03-07 16:24,14.3 secs,Planet Monthly Mosaics,,POINT(38.321928188337154 3.043383821526915),486,Non-crop
+487,487,39.34805366474005,0.5760758805466408,aasareansah@gmail.com,false,2023-03-07 16:24,7.9 secs,Planet Monthly Mosaics,,POINT(39.34805366474005 0.576075880546641),487,Non-crop
+488,488,40.70228392102145,2.2930049702457507,aasareansah@gmail.com,false,2023-03-07 16:24,11.6 secs,Planet Monthly Mosaics,,POINT(40.70228392102145 2.293004970245751),488,Non-crop
+489,489,34.988652426404,2.685077579713715,aasareansah@gmail.com,false,2023-03-07 16:25,10.8 secs,Planet Monthly Mosaics,,POINT(34.988652426404 2.685077579713715),489,Non-crop
+490,490,35.0036305876314,-0.7040441877978902,isha9a@umd.edu,false,2023-03-08 21:35,151.2 secs,Mapbox Satellite,,POINT(35.0036305876314 -0.70404418779789),490,Non-crop
+491,491,34.653443139440675,-0.02515374865425375,aasareansah@gmail.com,false,2023-03-07 16:25,13.7 secs,Planet Monthly Mosaics,,POINT(34.653443139440675 -0.025153748654254),491,Non-crop
+492,492,40.614414543790275,0.3435724730712614,aasareansah@gmail.com,false,2023-03-07 16:25,7.9 secs,Planet Monthly Mosaics,,POINT(40.614414543790275 0.343572473071261),492,Non-crop
+493,493,38.51702418202515,-2.8238115957339627,aasareansah@gmail.com,false,2023-03-07 16:25,8.9 secs,Planet Monthly Mosaics,,POINT(38.51702418202515 -2.823811595733963),493,Non-crop
+494,494,34.383934200549206,4.452384575378512,aasareansah@gmail.com,false,2023-03-07 16:25,8.8 secs,Planet Monthly Mosaics,,POINT(34.383934200549206 4.452384575378512),494,Non-crop
+495,495,37.70530305115963,2.5868444101204076,aasareansah@gmail.com,false,2023-03-07 16:25,7.2 secs,Planet Monthly Mosaics,,POINT(37.70530305115963 2.586844410120408),495,Non-crop
+496,496,35.33655489953117,-1.367002453719124,aasareansah@gmail.com,false,2023-03-07 16:25,7.7 secs,Planet Monthly Mosaics,,POINT(35.33655489953117 -1.367002453719124),496,Non-crop
+497,497,38.39506049495438,2.5714102466385724,aasareansah@gmail.com,false,2023-03-07 16:25,10.1 secs,Planet Monthly Mosaics,,POINT(38.39506049495438 2.571410246638572),497,Non-crop
+498,498,39.084849466631404,3.2272009282858454,aasareansah@gmail.com,false,2023-03-07 16:26,8.5 secs,Planet Monthly Mosaics,,POINT(39.084849466631404 3.227200928285845),498,Non-crop
+499,499,37.09093189496214,0.9915204603295135,aasareansah@gmail.com,false,2023-03-07 16:26,8.0 secs,Planet Monthly Mosaics,,POINT(37.09093189496214 0.991520460329514),499,Non-crop
+500,500,37.349874791030274,-2.6561425061602493,aasareansah@gmail.com,false,2023-03-07 16:26,13.6 secs,Planet Monthly Mosaics,,POINT(37.349874791030274 -2.656142506160249),500,Non-crop
+501,501,35.17501834522299,-0.7515855198732806,isha9a@umd.edu,false,2023-03-08 21:36,80.3 secs,Sentinel-2,,POINT(35.17501834522299 -0.751585519873281),501,Non-crop
+502,502,35.71291563450446,-0.6426003621868701,aasareansah@gmail.com,false,2023-03-07 16:26,10.2 secs,Planet Monthly Mosaics,,POINT(35.71291563450446 -0.64260036218687),502,Non-crop
+503,503,34.73484872536813,-0.8482526044699542,isha9a@umd.edu,false,2023-03-08 21:38,103.7 secs,Mapbox Satellite,,POINT(34.73484872536813 -0.848252604469954),503,Crop
+504,504,39.023752295479596,-1.6523111301971936,aasareansah@gmail.com,false,2023-03-07 16:27,8.9 secs,Planet Monthly Mosaics,,POINT(39.023752295479596 -1.652311130197194),504,Non-crop
+505,505,40.78764234677859,2.693632786095179,aasareansah@gmail.com,false,2023-03-07 16:27,8.7 secs,Planet Monthly Mosaics,,POINT(40.78764234677859 2.693632786095179),505,Non-crop
+506,506,35.06000147333601,-0.7966242423341563,aasareansah@gmail.com,false,2023-03-07 16:27,8.8 secs,Planet Monthly Mosaics,,POINT(35.06000147333601 -0.796624242334156),506,Non-crop
+507,507,36.30009993341707,-0.8232024117545933,aasareansah@gmail.com,false,2023-03-07 16:27,12.8 secs,Planet Monthly Mosaics,,POINT(36.30009993341707 -0.823202411754593),507,Non-crop
+508,508,35.10989219436885,-0.32787050503899345,aasareansah@gmail.com,false,2023-03-07 16:27,17.7 secs,Planet Monthly Mosaics,,POINT(35.10989219436885 -0.327870505038993),508,Crop
+509,509,34.828144212308935,-0.5773129039263161,aasareansah@gmail.com,false,2023-03-07 16:27,11.8 secs,Planet Monthly Mosaics,,POINT(34.828144212308935 -0.577312903926316),509,Crop
+510,510,35.97078220549668,2.9560655204795507,aasareansah@gmail.com,false,2023-03-07 16:27,10.1 secs,Planet Monthly Mosaics,,POINT(35.97078220549668 2.956065520479551),510,Non-crop
+511,511,35.10284238847304,-0.4966636896018876,aasareansah@gmail.com,false,2023-03-07 16:28,8.4 secs,Planet Monthly Mosaics,,POINT(35.10284238847304 -0.496663689601888),511,Non-crop
+512,512,40.132913213558695,-3.146251638192202,aasareansah@gmail.com,false,2023-03-07 16:28,19.5 secs,Planet Monthly Mosaics,,POINT(40.132913213558695 -3.146251638192202),512,Non-crop
+513,513,34.22388893299226,0.605241533460933,isha9a@umd.edu,false,2023-03-08 21:38,29.8 secs,Planet Monthly Mosaics,,POINT(34.22388893299226 0.605241533460933),513,Crop
+514,514,38.96670779650444,-0.4182302700090625,aasareansah@gmail.com,false,2023-03-07 16:28,9.9 secs,Planet Monthly Mosaics,,POINT(38.96670779650444 -0.418230270009062),514,Non-crop
+515,515,40.32965437189055,1.3051310381454755,aasareansah@gmail.com,false,2023-03-07 16:28,7.9 secs,Planet Monthly Mosaics,,POINT(40.32965437189055 1.305131038145476),515,Non-crop
+516,516,34.96478463720174,-0.5794803589111308,isha9a@umd.edu,false,2023-03-08 21:43,286.6 secs,Planet Monthly Mosaics,,POINT(34.96478463720174 -0.579480358911131),516,Non-crop
+517,517,40.96226183600945,-0.2882607556531856,aasareansah@gmail.com,false,2023-03-07 16:29,12.1 secs,Planet Monthly Mosaics,,POINT(40.96226183600945 -0.288260755653186),517,Non-crop
+518,518,39.735173373793074,3.116710754764235,aasareansah@gmail.com,false,2023-03-07 16:29,9.5 secs,Planet Monthly Mosaics,,POINT(39.735173373793074 3.116710754764235),518,Non-crop
+519,519,36.82035819397363,1.9556658604114368,isha9a@umd.edu,false,2023-03-08 21:43,31.8 secs,Mapbox Satellite,,POINT(36.82035819397363 1.955665860411437),519,Non-crop
+520,520,35.35383455962234,2.211003394302324,isha9a@umd.edu,false,2023-03-08 21:44,16.3 secs,Mapbox Satellite,,POINT(35.35383455962234 2.211003394302324),520,Non-crop
+521,521,35.410735825610544,-1.428738022679518,isha9a@umd.edu,false,2023-03-08 21:44,12.7 secs,Mapbox Satellite,,POINT(35.410735825610544 -1.428738022679518),521,Non-crop
+522,522,37.19939040219627,2.0807162473361056,isha9a@umd.edu,false,2023-03-08 21:44,31.6 secs,Mapbox Satellite,,POINT(37.19939040219627 2.080716247336106),522,Non-crop
+523,523,35.08208831520865,-0.9479913632950039,isha9a@umd.edu,false,2023-03-08 21:45,48.4 secs,Planet Monthly Mosaics,,POINT(35.08208831520865 -0.947991363295004),523,Crop
+524,524,36.529750333470275,-1.2510182266385572,isha9a@umd.edu,false,2023-03-08 21:46,33.4 secs,Mapbox Satellite,,POINT(36.529750333470275 -1.251018226638557),524,Non-crop
+525,525,34.93545090273626,1.3595428403458083,isha9a@umd.edu,false,2023-03-08 21:46,43.9 secs,Planet Monthly Mosaics,,POINT(34.93545090273626 1.359542840345808),525,Non-crop
+526,526,34.71477847358641,0.00774202647017627,isha9a@umd.edu,false,2023-03-08 21:48,86.0 secs,Mapbox Satellite,,POINT(34.71477847358641 0.007742026470176),526,Crop
+527,527,39.59186188819468,-1.1566710064881998,isha9a@umd.edu,false,2023-03-08 21:49,49.0 secs,Mapbox Satellite,,POINT(39.59186188819468 -1.1566710064882),527,Non-crop
+528,528,38.23861965981843,0.33277349267400996,isha9a@umd.edu,false,2023-03-08 21:49,18.7 secs,Mapbox Satellite,,POINT(38.23861965981843 0.33277349267401),528,Non-crop
+529,529,37.01526073538915,-0.7368748435057784,isha9a@umd.edu,false,2023-03-08 21:51,95.2 secs,Mapbox Satellite,,POINT(37.01526073538915 -0.736874843505778),529,Non-crop
+530,530,38.09145658119295,-2.415198363979528,isha9a@umd.edu,false,2023-03-08 21:56,336.6 secs,Planet Monthly Mosaics,,POINT(38.09145658119295 -2.415198363979528),530,Crop
+531,531,37.65093779234663,-0.026120886979007708,isha9a@umd.edu,false,2023-03-08 21:57,61.5 secs,Mapbox Satellite,,POINT(37.65093779234663 -0.026120886979008),531,Non-crop
+532,532,40.536095203749554,3.632313062717191,isha9a@umd.edu,false,2023-03-08 21:59,114.1 secs,Mapbox Satellite,,POINT(40.536095203749554 3.632313062717191),532,Non-crop
+533,533,34.56842069799163,0.8483574983470237,isha9a@umd.edu,false,2023-03-08 22:04,293.7 secs,Mapbox Satellite,,POINT(34.56842069799163 0.848357498347024),533,Crop
+534,534,34.560156122324,-0.01247945700671197,isha9a@umd.edu,false,2023-03-08 22:04,12.8 secs,Mapbox Satellite,,POINT(34.560156122324 -0.012479457006712),534,Crop
+535,535,36.52662305326271,1.9856940678472765,isha9a@umd.edu,false,2023-03-08 22:05,31.0 secs,Planet Monthly Mosaics,,POINT(36.52662305326271 1.985694067847276),535,Non-crop
+536,536,36.814465210482616,-1.0359101389648762,isha9a@umd.edu,false,2023-03-08 22:06,93.2 secs,Mapbox Satellite,,POINT(36.814465210482616 -1.035910138964876),536,Non-crop
+537,537,37.06018956428111,-0.4699689113578657,isha9a@umd.edu,false,2023-03-08 22:07,22.5 secs,Mapbox Satellite,,POINT(37.06018956428111 -0.469968911357866),537,Non-crop
+538,538,39.14612330417667,2.7377571101253593,isha9a@umd.edu,false,2023-03-08 22:07,28.3 secs,Planet Monthly Mosaics,,POINT(39.14612330417667 2.737757110125359),538,Non-crop
+539,539,37.87082220460906,-1.7089443100333617,isha9a@umd.edu,false,2023-03-08 22:08,30.8 secs,Mapbox Satellite,,POINT(37.87082220460906 -1.708944310033362),539,Non-crop
+540,540,34.88255928203005,-0.023363158733180508,isha9a@umd.edu,false,2023-03-08 22:08,23.5 secs,Mapbox Satellite,,POINT(34.88255928203005 -0.023363158733181),540,Non-crop
+541,541,34.46410083114127,-0.8173018845840876,isha9a@umd.edu,false,2023-03-08 22:09,77.0 secs,Mapbox Satellite,,POINT(34.46410083114127 -0.817301884584088),541,Crop
+542,542,34.93492776673225,2.8648969176507055,isha9a@umd.edu,false,2023-03-08 22:09,10.7 secs,Mapbox Satellite,,POINT(34.93492776673225 2.864896917650706),542,Non-crop
+543,543,38.81338965106459,1.119704331352858,isha9a@umd.edu,false,2023-03-08 22:09,9.2 secs,Mapbox Satellite,,POINT(38.81338965106459 1.119704331352858),543,Non-crop
diff --git a/data/ref_samples_area/ceo-Kenya-Crop-Area-Estimation-Reference-Sample-2019---Set-2-sample-data-2024-03-14.csv b/data/ref_samples_area/ceo-Kenya-Crop-Area-Estimation-Reference-Sample-2019---Set-2-sample-data-2024-03-14.csv
new file mode 100644
index 00000000..9087c61d
--- /dev/null
+++ b/data/ref_samples_area/ceo-Kenya-Crop-Area-Estimation-Reference-Sample-2019---Set-2-sample-data-2024-03-14.csv
@@ -0,0 +1,545 @@
+plotid,sampleid,lon,lat,email,flagged,collection_time,analysis_duration,imagery_title,imagery_attributions,sample_geom,pl_sampleid,Does this point correspond to active cropland?
+0,0,34.988629829555656,2.403552919494441,adadebay@umd.edu,false,2023-03-06 13:50,5.3 secs,,,POINT(34.988629829555656 2.403552919494441),0,Non-crop
+1,1,40.4369134463712,0.23745156614928092,adadebay@umd.edu,false,2023-03-06 13:50,6.3 secs,,,POINT(40.4369134463712 0.237451566149281),1,Non-crop
+2,2,35.525785266678824,4.203260690691489,adadebay@umd.edu,false,2023-03-06 13:50,21.8 secs,,,POINT(35.525785266678824 4.203260690691489),2,Non-crop
+3,3,35.34502880181526,1.7861325423989396,adadebay@umd.edu,false,2023-02-28 20:29,8.1 secs,Mapbox Satellite,,POINT(35.34502880181526 1.78613254239894),3,Non-crop
+4,4,41.092915614480624,-2.088706573827987,adadebay@umd.edu,false,2023-02-28 20:30,29.2 secs,Mapbox Satellite,,POINT(41.092915614480624 -2.088706573827987),4,Crop
+5,5,34.4249103574835,-1.030559071942206,adadebay@umd.edu,false,2023-02-28 20:31,75.8 secs,Mapbox Satellite,,POINT(34.4249103574835 -1.030559071942206),5,Crop
+6,6,39.74092423396898,0.4906842425498013,adadebay@umd.edu,false,2023-02-28 20:31,33.5 secs,Mapbox Satellite,,POINT(39.74092423396898 0.490684242549801),6,Non-crop
+7,7,35.17727471050547,0.8244599235574998,adadebay@umd.edu,false,2023-02-28 20:32,26.9 secs,Planet Monthly Mosaics,,POINT(35.17727471050547 0.8244599235575),7,Crop
+8,8,39.45538305881567,1.1210267423453937,adadebay@umd.edu,false,2023-02-28 20:32,8.4 secs,Planet Monthly Mosaics,,POINT(39.45538305881567 1.121026742345394),8,Non-crop
+9,9,39.369209788785,-2.6825226398269617,adadebay@umd.edu,false,2023-02-28 20:32,13.6 secs,Planet Monthly Mosaics,,POINT(39.369209788785 -2.682522639826962),9,Non-crop
+10,10,34.7476597030811,3.302863534933985,dianafrimpong710@gmail.com,false,2023-03-01 20:12,105.2 secs,Planet Monthly Mosaics,,POINT(34.7476597030811 3.302863534933985),10,Non-crop
+11,11,37.52023980882631,1.1294265556977394,adadebay@umd.edu,false,2023-02-28 20:33,10.5 secs,Planet Monthly Mosaics,,POINT(37.52023980882631 1.129426555697739),11,Non-crop
+12,12,40.04232638214461,0.10707860224488248,adadebay@umd.edu,false,2023-02-28 20:33,8.9 secs,Planet Monthly Mosaics,,POINT(40.04232638214461 0.107078602244882),12,Non-crop
+13,13,36.59504709600916,2.079095336922312,adadebay@umd.edu,false,2023-02-28 20:33,7.9 secs,Planet Monthly Mosaics,,POINT(36.59504709600916 2.079095336922312),13,Non-crop
+14,14,35.355886022749836,-1.7191142947031781,adadebay@umd.edu,false,2023-02-28 20:33,23.5 secs,Mapbox Satellite,,POINT(35.355886022749836 -1.719114294703178),14,Non-crop
+15,15,37.94355071711686,0.2736254374888986,adadebay@umd.edu,false,2023-02-28 20:34,8.4 secs,Mapbox Satellite,,POINT(37.94355071711686 0.273625437488899),15,Crop
+16,16,35.567931891106554,-0.963807053974099,adadebay@umd.edu,false,2023-02-28 20:38,278.5 secs,Planet Monthly Mosaics,,POINT(35.567931891106554 -0.963807053974099),16,Crop
+17,17,35.2370463385883,0.23539226037375338,adadebay@umd.edu,false,2023-02-28 20:39,34.4 secs,Planet Monthly Mosaics,,POINT(35.2370463385883 0.235392260373753),17,Crop
+18,18,34.56192216282922,-0.5423313934257865,adadebay@umd.edu,false,2023-02-28 20:44,328.6 secs,Planet Monthly Mosaics,,POINT(34.56192216282922 -0.542331393425786),18,Crop
+19,19,39.91309533587102,0.5405029647849312,adadebay@umd.edu,false,2023-02-28 20:44,12.0 secs,Planet Monthly Mosaics,,POINT(39.91309533587102 0.540502964784931),19,Non-crop
+20,20,37.77622013677315,-3.216077832453689,adadebay@umd.edu,false,2023-02-28 20:45,13.0 secs,Planet Monthly Mosaics,,POINT(37.77622013677315 -3.216077832453689),20,Non-crop
+21,21,36.952890064644144,-0.7336035276099032,adadebay@umd.edu,false,2023-02-28 20:50,279.6 secs,Mapbox Satellite,,POINT(36.952890064644144 -0.733603527609903),21,Crop
+22,22,40.81069538939396,3.8142341143013847,adadebay@umd.edu,false,2023-02-28 20:50,11.1 secs,Mapbox Satellite,,POINT(40.81069538939396 3.814234114301385),22,Non-crop
+23,23,39.31291565564592,-4.292596583003278,adadebay@umd.edu,false,2023-02-28 20:51,19.5 secs,Mapbox Satellite,,POINT(39.31291565564592 -4.292596583003278),23,Non-crop
+24,24,36.56392047775658,-1.4401680504539716,adadebay@umd.edu,false,2023-02-28 20:51,12.1 secs,Mapbox Satellite,,POINT(36.56392047775658 -1.440168050453972),24,Non-crop
+25,25,40.761482926934235,1.797142812318858,adadebay@umd.edu,false,2023-02-28 20:51,7.1 secs,Mapbox Satellite,,POINT(40.761482926934235 1.797142812318858),25,Non-crop
+26,26,35.157061444706144,1.0119532740001498,adadebay@umd.edu,false,2023-02-28 20:52,61.0 secs,Planet Monthly Mosaics,,POINT(35.157061444706144 1.01195327400015),26,Non-crop
+27,27,34.203510614976835,3.843240810122485,adadebay@umd.edu,false,2023-02-28 20:52,11.2 secs,Planet Monthly Mosaics,,POINT(34.203510614976835 3.843240810122485),27,Non-crop
+28,28,40.634491221723714,1.4292896941865394,adadebay@umd.edu,false,2023-02-28 20:52,8.5 secs,Planet Monthly Mosaics,,POINT(40.634491221723714 1.429289694186539),28,Non-crop
+29,29,37.24315858180454,0.18885273302287983,adadebay@umd.edu,false,2023-02-28 20:53,19.8 secs,Mapbox Satellite,,POINT(37.24315858180454 0.18885273302288),29,Non-crop
+30,30,38.50529869815832,1.3266174643205182,adadebay@umd.edu,false,2023-02-28 20:53,9.2 secs,Mapbox Satellite,,POINT(38.50529869815832 1.326617464320518),30,Non-crop
+31,31,40.02570086581538,2.052485246929315,adadebay@umd.edu,false,2023-02-28 20:53,17.0 secs,Mapbox Satellite,,POINT(40.02570086581538 2.052485246929315),31,Non-crop
+32,32,36.08923467501349,2.8152333378024177,adadebay@umd.edu,false,2023-02-28 20:53,8.1 secs,Mapbox Satellite,,POINT(36.08923467501349 2.815233337802418),32,Non-crop
+33,33,38.25468798085764,-3.293961403681881,adadebay@umd.edu,false,2023-02-28 20:53,7.1 secs,Mapbox Satellite,,POINT(38.25468798085764 -3.293961403681881),33,Non-crop
+34,34,36.24856761389988,2.978220395675694,adadebay@umd.edu,false,2023-02-28 20:53,6.4 secs,Mapbox Satellite,,POINT(36.24856761389988 2.978220395675694),34,Non-crop
+35,35,35.08856440679308,-0.610412869850806,adadebay@umd.edu,false,2023-02-28 20:53,9.1 secs,Mapbox Satellite,,POINT(35.08856440679308 -0.610412869850806),35,Non-crop
+36,36,37.10586175136883,0.6450479726392292,adadebay@umd.edu,false,2023-02-28 20:54,14.6 secs,Mapbox Satellite,,POINT(37.10586175136883 0.645047972639229),36,Non-crop
+37,37,36.28157017849977,-0.844978485851314,adadebay@umd.edu,false,2023-02-28 20:54,12.6 secs,Mapbox Satellite,,POINT(36.28157017849977 -0.844978485851314),37,Crop
+38,38,35.24641350935284,3.1088112936312293,adadebay@umd.edu,false,2023-02-28 20:54,12.1 secs,Mapbox Satellite,,POINT(35.24641350935284 3.108811293631229),38,Non-crop
+39,39,35.725864381252485,-0.9901508750054767,adadebay@umd.edu,false,2023-02-28 20:54,7.7 secs,Mapbox Satellite,,POINT(35.725864381252485 -0.990150875005477),39,Crop
+40,40,36.68065734363421,-0.8149776658973387,adadebay@umd.edu,false,2023-02-28 20:54,7.6 secs,Mapbox Satellite,,POINT(36.68065734363421 -0.814977665897339),40,Non-crop
+41,41,39.78768478044772,3.532698290163748,adadebay@umd.edu,false,2023-02-28 20:54,7.8 secs,Mapbox Satellite,,POINT(39.78768478044772 3.532698290163748),41,Non-crop
+42,42,34.937338523086936,1.1808045765140807,adadebay@umd.edu,false,2023-02-28 20:55,8.7 secs,Mapbox Satellite,,POINT(34.937338523086936 1.180804576514081),42,Crop
+43,43,35.95117962367605,0.11765502410130428,adadebay@umd.edu,false,2023-02-28 20:55,37.5 secs,Planet Monthly Mosaics,,POINT(35.95117962367605 0.117655024101304),43,Crop
+44,44,36.233816518951166,-0.9529835747040568,adadebay@umd.edu,false,2023-02-28 20:56,32.4 secs,Mapbox Satellite,,POINT(36.233816518951166 -0.952983574704057),44,Non-crop
+45,45,40.34542919363465,-0.16642752812001055,adadebay@umd.edu,false,2023-02-28 20:56,6.8 secs,Mapbox Satellite,,POINT(40.34542919363465 -0.166427528120011),45,Non-crop
+46,46,35.109574883126434,1.7949982414224284,adadebay@umd.edu,false,2023-02-28 20:56,7.2 secs,Mapbox Satellite,,POINT(35.109574883126434 1.794998241422428),46,Non-crop
+47,47,38.10104270259982,2.2770658661008274,adadebay@umd.edu,false,2023-02-28 20:56,7.5 secs,Mapbox Satellite,,POINT(38.10104270259982 2.277065866100827),47,Non-crop
+48,48,34.93916270476336,-0.5632779940672789,adadebay@umd.edu,false,2023-02-28 20:56,21.3 secs,Mapbox Satellite,,POINT(34.93916270476336 -0.563277994067279),48,Non-crop
+49,49,35.698150869472485,2.656619125017031,adadebay@umd.edu,false,2023-02-28 20:57,9.1 secs,Mapbox Satellite,,POINT(35.698150869472485 2.656619125017031),49,Non-crop
+50,50,38.332830459210996,-2.4627477963661004,adadebay@umd.edu,false,2023-02-28 20:57,8.2 secs,Mapbox Satellite,,POINT(38.332830459210996 -2.4627477963661),50,Non-crop
+51,51,36.384083118334274,4.384630880395623,adadebay@umd.edu,false,2023-02-28 20:57,8.3 secs,Mapbox Satellite,,POINT(36.384083118334274 4.384630880395623),51,Non-crop
+52,52,35.01471998180397,-0.42170819623595546,adadebay@umd.edu,false,2023-02-28 20:57,19.5 secs,Mapbox Satellite,,POINT(35.01471998180397 -0.421708196235955),52,Non-crop
+53,53,37.47142387421962,2.60955944432736,adadebay@umd.edu,false,2023-02-28 20:57,8.9 secs,Mapbox Satellite,,POINT(37.47142387421962 2.60955944432736),53,Non-crop
+54,54,37.44533838762773,-1.8552448087267317,adadebay@umd.edu,false,2023-02-28 20:58,15.7 secs,Mapbox Satellite,,POINT(37.44533838762773 -1.855244808726732),54,Non-crop
+55,55,40.14514392119623,-3.098656053349216,adadebay@umd.edu,false,2023-02-28 21:00,149.6 secs,Mapbox Satellite,,POINT(40.14514392119623 -3.098656053349216),55,Non-crop
+56,56,38.6594746226374,-3.835613952172189,adadebay@umd.edu,false,2023-02-28 21:00,11.1 secs,Mapbox Satellite,,POINT(38.6594746226374 -3.835613952172189),56,Non-crop
+57,57,39.02562274024034,0.07077064319861262,adadebay@umd.edu,false,2023-02-28 21:00,7.3 secs,Mapbox Satellite,,POINT(39.02562274024034 0.070770643198613),57,Non-crop
+58,58,36.02109758574353,3.5366108060174963,adadebay@umd.edu,false,2023-02-28 21:00,5.2 secs,Mapbox Satellite,,POINT(36.02109758574353 3.536610806017496),58,Non-crop
+59,59,35.06865100448405,1.187649341007699,adadebay@umd.edu,false,2023-02-28 21:04,231.0 secs,Mapbox Satellite,,POINT(35.06865100448405 1.187649341007699),59,Non-crop
+60,60,38.263410192690564,2.051383658830262,adadebay@umd.edu,false,2023-02-28 21:04,11.8 secs,Mapbox Satellite,,POINT(38.263410192690564 2.051383658830262),60,Non-crop
+61,61,40.14898007195484,3.3226177399453816,adadebay@umd.edu,false,2023-02-28 21:05,16.1 secs,Mapbox Satellite,,POINT(40.14898007195484 3.322617739945382),61,Non-crop
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+63,63,36.33551213412802,-2.0059902859724703,adadebay@umd.edu,false,2023-02-28 21:05,12.3 secs,Mapbox Satellite,,POINT(36.33551213412802 -2.00599028597247),63,Non-crop
+64,64,37.81898292849584,2.890470600118884,adadebay@umd.edu,false,2023-02-28 21:05,11.1 secs,Mapbox Satellite,,POINT(37.81898292849584 2.890470600118884),64,Non-crop
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+67,67,35.12381890958649,0.7606469113885553,adadebay@umd.edu,false,2023-02-28 21:06,9.4 secs,Mapbox Satellite,,POINT(35.12381890958649 0.760646911388555),67,Non-crop
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+69,69,36.6457343594037,-0.49640885252982253,adadebay@umd.edu,false,2023-02-28 21:06,11.1 secs,Mapbox Satellite,,POINT(36.6457343594037 -0.496408852529823),69,Non-crop
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+73,73,34.793844712321096,1.0897067468642034,adadebay@umd.edu,false,2023-02-28 21:08,13.6 secs,Mapbox Satellite,,POINT(34.793844712321096 1.089706746864203),73,Crop
+74,74,35.82894420505845,0.4963590966445787,adadebay@umd.edu,false,2023-02-28 21:08,23.3 secs,Mapbox Satellite,,POINT(35.82894420505845 0.496359096644579),74,Non-crop
+75,75,40.86680566262454,2.5510833756481532,adadebay@umd.edu,false,2023-02-28 21:08,21.3 secs,Mapbox Satellite,,POINT(40.86680566262454 2.551083375648153),75,Non-crop
+76,76,37.42434808348648,2.172412965946544,adadebay@umd.edu,false,2023-02-28 21:08,9.9 secs,Mapbox Satellite,,POINT(37.42434808348648 2.172412965946544),76,Non-crop
+77,77,37.15129430727518,-0.7226093010817065,adadebay@umd.edu,false,2023-02-28 21:09,41.4 secs,Mapbox Satellite,,POINT(37.15129430727518 -0.722609301081706),77,Non-crop
+78,78,35.24400762282487,-0.8344837781524341,adadebay@umd.edu,false,2023-02-28 21:10,38.8 secs,Mapbox Satellite,,POINT(35.24400762282487 -0.834483778152434),78,Non-crop
+79,79,35.89383258961963,-0.3086155983480622,adadebay@umd.edu,false,2023-02-28 21:10,26.0 secs,Mapbox Satellite,,POINT(35.89383258961963 -0.308615598348062),79,Crop
+80,80,34.8374317772852,1.2105020095980699,adadebay@umd.edu,false,2023-02-28 21:10,11.8 secs,Mapbox Satellite,,POINT(34.8374317772852 1.21050200959807),80,Crop
+81,81,37.47851943218349,-1.5673861189284093,adadebay@umd.edu,false,2023-02-28 21:12,73.3 secs,Planet Monthly Mosaics,,POINT(37.47851943218349 -1.567386118928409),81,Crop
+82,82,37.62528047925142,-3.386462077890213,adadebay@umd.edu,false,2023-02-28 21:12,19.3 secs,Mapbox Satellite,,POINT(37.62528047925142 -3.386462077890213),82,Non-crop
+83,83,37.94051901411475,0.003977377954060247,adadebay@umd.edu,false,2023-02-28 21:13,32.1 secs,Mapbox Satellite,,POINT(37.94051901411475 0.00397737795406),83,Non-crop
+84,84,38.7070591354366,-2.0059845289306772,adadebay@umd.edu,false,2023-02-28 21:13,8.9 secs,Mapbox Satellite,,POINT(38.7070591354366 -2.005984528930677),84,Non-crop
+85,85,34.838779927856656,-0.86257077504155,adadebay@umd.edu,false,2023-02-28 21:13,10.0 secs,Mapbox Satellite,,POINT(34.838779927856656 -0.86257077504155),85,Crop
+86,86,37.44660412263658,1.2193124117332885,adadebay@umd.edu,false,2023-02-28 21:13,7.3 secs,Mapbox Satellite,,POINT(37.44660412263658 1.219312411733288),86,Non-crop
+87,87,34.6632601985172,0.07559985882881662,adadebay@umd.edu,false,2023-02-28 21:13,9.7 secs,Mapbox Satellite,,POINT(34.6632601985172 0.075599858828817),87,Non-crop
+88,88,34.638949502003825,0.6729112014973999,adadebay@umd.edu,false,2023-02-28 21:13,8.3 secs,Mapbox Satellite,,POINT(34.638949502003825 0.6729112014974),88,Crop
+89,89,34.5994023707549,0.7630158568229832,adadebay@umd.edu,false,2023-02-28 21:14,31.3 secs,Mapbox Satellite,,POINT(34.5994023707549 0.763015856822983),89,Crop
+90,90,36.93706274251377,2.255505688906279,adadebay@umd.edu,false,2023-02-28 21:14,8.4 secs,Mapbox Satellite,,POINT(36.93706274251377 2.255505688906279),90,Non-crop
+91,91,40.44223583641046,4.03082660667592,adadebay@umd.edu,false,2023-02-28 21:14,6.8 secs,Mapbox Satellite,,POINT(40.44223583641046 4.03082660667592),91,Non-crop
+92,92,36.06039300271393,-0.5684581218619031,adadebay@umd.edu,false,2023-02-28 21:14,6.7 secs,Mapbox Satellite,,POINT(36.06039300271393 -0.568458121861903),92,Crop
+93,93,37.59588686792248,0.11931919114708699,adadebay@umd.edu,false,2023-02-28 21:14,8.2 secs,Mapbox Satellite,,POINT(37.59588686792248 0.119319191147087),93,Non-crop
+94,94,34.95006733497294,-0.7711616777128759,adadebay@umd.edu,false,2023-02-28 21:14,7.7 secs,Mapbox Satellite,,POINT(34.95006733497294 -0.771161677712876),94,Crop
+95,95,34.37280204804135,-0.5213450533332684,adadebay@umd.edu,false,2023-02-28 21:15,11.7 secs,Mapbox Satellite,,POINT(34.37280204804135 -0.521345053333268),95,Non-crop
+96,96,35.92793233525755,-2.0132818618461816,adadebay@umd.edu,false,2023-02-28 21:15,8.8 secs,Mapbox Satellite,,POINT(35.92793233525755 -2.013281861846182),96,Non-crop
+97,97,40.50489472621563,-1.7350862750790679,adadebay@umd.edu,false,2023-02-28 21:15,9.1 secs,Mapbox Satellite,,POINT(40.50489472621563 -1.735086275079068),97,Non-crop
+98,98,34.84343325317191,-0.5513431781255153,adadebay@umd.edu,false,2023-02-28 21:15,9.0 secs,Mapbox Satellite,,POINT(34.84343325317191 -0.551343178125515),98,Crop
+99,99,38.810924566797794,2.4564363287855104,adadebay@umd.edu,false,2023-02-28 21:15,6.4 secs,Mapbox Satellite,,POINT(38.810924566797794 2.45643632878551),99,Non-crop
+100,100,38.82801401094352,0.44583460966597566,adadebay@umd.edu,false,2023-02-28 21:15,5.4 secs,Mapbox Satellite,,POINT(38.82801401094352 0.445834609665976),100,Non-crop
+101,101,35.01842360551604,1.951978852908563,adadebay@umd.edu,false,2023-02-28 21:15,5.8 secs,Mapbox Satellite,,POINT(35.01842360551604 1.951978852908563),101,Non-crop
+102,102,38.63846258841169,-1.8744628832696686,adadebay@umd.edu,false,2023-02-28 21:15,8.7 secs,Mapbox Satellite,,POINT(38.63846258841169 -1.874462883269669),102,Non-crop
+103,103,34.67637175915808,4.509081535107014,adadebay@umd.edu,false,2023-02-28 21:16,10.6 secs,Mapbox Satellite,,POINT(34.67637175915808 4.509081535107014),103,Non-crop
+104,104,34.80059714936316,-0.6330272098164963,adadebay@umd.edu,false,2023-02-28 21:16,40.0 secs,Mapbox Satellite,,POINT(34.80059714936316 -0.633027209816496),104,Crop
+105,105,39.03989200445923,-2.346187888919231,adadebay@umd.edu,false,2023-02-28 21:16,14.4 secs,Mapbox Satellite,,POINT(39.03989200445923 -2.346187888919231),105,Non-crop
+106,106,37.75101158485352,-0.05595061740159739,adadebay@umd.edu,false,2023-02-28 21:17,24.9 secs,Mapbox Satellite,,POINT(37.75101158485352 -0.055950617401597),106,Non-crop
+107,107,34.94705324098189,-0.5166194835899087,adadebay@umd.edu,false,2023-02-28 21:19,141.7 secs,Mapbox Satellite,,POINT(34.94705324098189 -0.516619483589909),107,Non-crop
+108,108,36.43853260211107,3.97061768786652,adadebay@umd.edu,false,2023-02-28 21:19,14.7 secs,Mapbox Satellite,,POINT(36.43853260211107 3.97061768786652),108,Non-crop
+109,109,38.8861466691318,3.0445834421692615,adadebay@umd.edu,false,2023-02-28 21:20,8.8 secs,Mapbox Satellite,,POINT(38.8861466691318 3.044583442169262),109,Non-crop
+110,110,35.876614895369954,3.7678862374880135,adadebay@umd.edu,false,2023-02-28 21:20,6.0 secs,Mapbox Satellite,,POINT(35.876614895369954 3.767886237488014),110,Non-crop
+111,111,38.82883825639645,2.5306209549193475,adadebay@umd.edu,false,2023-02-28 21:20,7.2 secs,Mapbox Satellite,,POINT(38.82883825639645 2.530620954919348),111,Non-crop
+112,112,38.2367270952654,-1.3301300190469625,adadebay@umd.edu,false,2023-02-28 21:20,8.6 secs,Mapbox Satellite,,POINT(38.2367270952654 -1.330130019046962),112,Non-crop
+113,113,37.63554202932443,0.03299322115736923,adadebay@umd.edu,false,2023-02-28 21:21,88.1 secs,Mapbox Satellite,,POINT(37.63554202932443 0.032993221157369),113,Crop
+114,114,37.05135107858711,-0.388438674158326,adadebay@umd.edu,false,2023-02-28 21:23,99.0 secs,Mapbox Satellite,,POINT(37.05135107858711 -0.388438674158326),114,Crop
+115,115,38.39683562140306,-3.562375659885144,adadebay@umd.edu,false,2023-02-28 21:23,9.3 secs,Mapbox Satellite,,POINT(38.39683562140306 -3.562375659885144),115,Crop
+116,116,38.47807537401632,-1.3194672935579193,adadebay@umd.edu,false,2023-02-28 21:23,8.1 secs,Mapbox Satellite,,POINT(38.47807537401632 -1.319467293557919),116,Non-crop
+117,117,37.90724334671853,1.2126009407086038,adadebay@umd.edu,false,2023-02-28 21:24,13.1 secs,Mapbox Satellite,,POINT(37.90724334671853 1.212600940708604),117,Non-crop
+118,118,35.1493469381087,0.7857613956282058,adadebay@umd.edu,false,2023-02-28 21:24,11.7 secs,Mapbox Satellite,,POINT(35.1493469381087 0.785761395628206),118,Crop
+119,119,37.04107541888969,2.415639616416417,adadebay@umd.edu,false,2023-02-28 21:24,9.1 secs,Mapbox Satellite,,POINT(37.04107541888969 2.415639616416417),119,Non-crop
+120,120,37.135914893319935,0.8892938871269861,adadebay@umd.edu,false,2023-02-28 21:24,7.7 secs,Mapbox Satellite,,POINT(37.135914893319935 0.889293887126986),120,Non-crop
+121,121,34.280251153267145,-0.8857188443090579,adadebay@umd.edu,false,2023-02-28 21:24,8.4 secs,Mapbox Satellite,,POINT(34.280251153267145 -0.885718844309058),121,Crop
+122,122,37.104919656283776,0.944772577359767,adadebay@umd.edu,false,2023-02-28 21:24,9.7 secs,Mapbox Satellite,,POINT(37.104919656283776 0.944772577359767),122,Crop
+123,123,36.91642788302333,-1.8734338149965883,adadebay@umd.edu,false,2023-02-28 21:24,9.6 secs,Mapbox Satellite,,POINT(36.91642788302333 -1.873433814996588),123,Non-crop
+124,124,35.6530472090822,1.3337481691783033,adadebay@umd.edu,false,2023-02-28 21:25,10.5 secs,Mapbox Satellite,,POINT(35.6530472090822 1.333748169178303),124,Non-crop
+125,125,37.665284200034286,-0.8743618235367431,adadebay@umd.edu,false,2023-02-28 21:34,590.8 secs,Mapbox Satellite,,POINT(37.665284200034286 -0.874361823536743),125,Non-crop
+126,126,40.28931527278772,-1.6826194889901331,adadebay@umd.edu,false,2023-02-28 21:35,7.8 secs,Mapbox Satellite,,POINT(40.28931527278772 -1.682619488990133),126,Non-crop
+127,127,35.24886337281288,0.9780628088660805,adadebay@umd.edu,false,2023-02-28 21:36,112.4 secs,Mapbox Satellite,,POINT(35.24886337281288 0.97806280886608),127,Crop
+128,128,35.350804743305915,-0.14292567584629862,adadebay@umd.edu,false,2023-02-28 21:37,8.9 secs,Mapbox Satellite,,POINT(35.350804743305915 -0.142925675846299),128,Crop
+129,129,36.875372636897794,-1.9178813516888036,adadebay@umd.edu,false,2023-02-28 21:37,13.2 secs,Mapbox Satellite,,POINT(36.875372636897794 -1.917881351688804),129,Non-crop
+130,130,36.36696602805077,-1.0011713498446717,adadebay@umd.edu,false,2023-02-28 21:37,14.2 secs,Mapbox Satellite,,POINT(36.36696602805077 -1.001171349844672),130,Non-crop
+131,131,34.03567997149679,-0.45059458138419733,adadebay@umd.edu,false,2023-02-28 21:37,10.0 secs,Mapbox Satellite,,POINT(34.03567997149679 -0.450594581384197),131,Non-crop
+132,132,34.31052408060243,0.06011131997460628,adadebay@umd.edu,false,2023-02-28 21:37,16.5 secs,Mapbox Satellite,,POINT(34.31052408060243 0.060111319974606),132,Non-crop
+133,133,40.112200611852735,3.057621068313114,adadebay@umd.edu,false,2023-03-01 20:11,11.3 secs,Mapbox Satellite,,POINT(40.112200611852735 3.057621068313114),133,Non-crop
+134,134,37.59446156272681,-0.6348207832764996,adadebay@umd.edu,false,2023-03-01 20:12,39.1 secs,Mapbox Satellite,,POINT(37.59446156272681 -0.6348207832765),134,Non-crop
+135,135,40.9961063143978,-1.6779669321206159,dianafrimpong710@gmail.com,false,2023-03-01 20:12,46.4 secs,Planet Monthly Mosaics,,POINT(40.9961063143978 -1.677966932120616),135,Non-crop
+136,136,39.22212647015637,2.6922268323855563,adadebay@umd.edu,false,2023-03-01 20:12,5.6 secs,Mapbox Satellite,,POINT(39.22212647015637 2.692226832385556),136,Non-crop
+137,137,36.21832154143681,-0.33166413329927724,adadebay@umd.edu,false,2023-03-01 20:12,49.2 secs,Planet Monthly Mosaics,,POINT(36.21832154143681 -0.331664133299277),137,Crop
+138,138,34.51475222889342,-0.7822238210290815,adadebay@umd.edu,false,2023-03-01 20:13,35.1 secs,Mapbox Satellite,,POINT(34.51475222889342 -0.782223821029082),138,Crop
+139,139,38.50386682037691,-0.7440523952127349,dianafrimpong710@gmail.com,false,2023-03-01 20:14,126.1 secs,Sentinel-2,,POINT(38.50386682037691 -0.744052395212735),139,Crop
+140,140,34.544980328359806,3.3127536927240926,adadebay@umd.edu,false,2023-03-01 20:54,9.0 secs,Mapbox Satellite,,POINT(34.544980328359806 3.312753692724093),140,Non-crop
+141,141,35.072614057925755,2.0515640292935595,dianafrimpong710@gmail.com,false,2023-03-01 20:15,40.5 secs,Planet Monthly Mosaics,,POINT(35.072614057925755 2.05156402929356),141,Non-crop
+142,142,37.63731851488094,-0.7082741543121748,dianafrimpong710@gmail.com,false,2023-03-01 20:16,59.9 secs,Planet Monthly Mosaics,,POINT(37.63731851488094 -0.708274154312175),142,Non-crop
+143,143,36.79356032174582,-1.3320484579772995,dianafrimpong710@gmail.com,false,2023-03-01 20:16,26.8 secs,Planet Monthly Mosaics,,POINT(36.79356032174582 -1.3320484579773),143,Non-crop
+144,144,37.196987405380554,-0.997502551481028,adadebay@umd.edu,false,2023-03-01 20:54,10.5 secs,Mapbox Satellite,,POINT(37.196987405380554 -0.997502551481028),144,Crop
+145,145,35.97558762159962,-0.12276447907124412,dianafrimpong710@gmail.com,false,2023-03-01 20:18,21.7 secs,Planet Monthly Mosaics,,POINT(35.97558762159962 -0.122764479071244),145,Crop
+146,146,34.1891721541313,-0.6146176829171154,dianafrimpong710@gmail.com,false,2023-03-01 20:20,114.5 secs,Planet Monthly Mosaics,,POINT(34.1891721541313 -0.614617682917115),146,Non-crop
+147,147,36.92847527942525,1.2782331263275961,dianafrimpong710@gmail.com,false,2023-03-01 20:21,18.2 secs,Planet Monthly Mosaics,,POINT(36.92847527942525 1.278233126327596),147,Non-crop
+148,148,37.67795253138457,-0.29646401880195755,adadebay@umd.edu,false,2023-03-01 20:55,55.8 secs,Planet Monthly Mosaics,,POINT(37.67795253138457 -0.296464018801958),148,Non-crop
+149,149,38.06462053176975,-1.043559942579206,dianafrimpong710@gmail.com,false,2023-03-01 20:24,110.5 secs,Planet Monthly Mosaics,,POINT(38.06462053176975 -1.043559942579206),149,Non-crop
+150,150,34.886305931598756,1.5162521774427242,dianafrimpong710@gmail.com,false,2023-03-01 20:24,19.2 secs,Planet Monthly Mosaics,,POINT(34.886305931598756 1.516252177442724),150,Non-crop
+151,151,36.96408686642167,0.4599662921332445,dianafrimpong710@gmail.com,false,2023-03-01 20:24,33.6 secs,Planet Monthly Mosaics,,POINT(36.96408686642167 0.459966292133244),151,Non-crop
+152,152,35.13520286056352,-1.1297140056307589,dianafrimpong710@gmail.com,false,2023-03-01 20:26,101.1 secs,Sentinel-2,,POINT(35.13520286056352 -1.129714005630759),152,Crop
+153,153,41.800681225973825,3.9420204760538824,dianafrimpong710@gmail.com,false,2023-03-01 20:26,30.5 secs,Planet Monthly Mosaics,,POINT(41.800681225973825 3.942020476053882),153,Non-crop
+154,154,36.41868031095638,-1.6763774091068298,dianafrimpong710@gmail.com,false,2023-03-01 20:27,52.5 secs,Planet Monthly Mosaics,,POINT(36.41868031095638 -1.67637740910683),154,Non-crop
+155,155,38.033900036116925,-1.3282559996083236,dianafrimpong710@gmail.com,false,2023-03-01 20:28,52.6 secs,Planet Monthly Mosaics,,POINT(38.033900036116925 -1.328255999608324),155,Non-crop
+156,156,39.29015146450414,-4.3996825162698165,adadebay@umd.edu,false,2023-03-01 20:59,257.8 secs,Mapbox Satellite,,POINT(39.29015146450414 -4.399682516269816),156,Crop
+157,157,37.44623545167376,-1.0728680549928664,dianafrimpong710@gmail.com,false,2023-03-01 20:32,66.2 secs,Planet Monthly Mosaics,,POINT(37.44623545167376 -1.072868054992866),157,Non-crop
+158,158,40.00198076172898,-1.4034050963748328,dianafrimpong710@gmail.com,false,2023-03-01 20:34,128.8 secs,Sentinel-2,,POINT(40.00198076172898 -1.403405096374833),158,Crop
+159,159,34.51540863900097,0.20085040451937342,dianafrimpong710@gmail.com,false,2023-03-01 20:36,133.9 secs,Sentinel-2,,POINT(34.51540863900097 0.200850404519373),159,Crop
+160,160,34.364068159964674,-0.027030376910205087,dianafrimpong710@gmail.com,false,2023-03-01 20:37,73.3 secs,Sentinel-2,,POINT(34.364068159964674 -0.027030376910205),160,Crop
+161,161,38.97895604283259,2.4386926635852486,dianafrimpong710@gmail.com,false,2023-03-01 20:38,26.4 secs,Planet Monthly Mosaics,,POINT(38.97895604283259 2.438692663585249),161,Non-crop
+162,162,35.27387999609261,0.11199548140903959,dianafrimpong710@gmail.com,false,2023-03-01 20:38,19.0 secs,Planet Monthly Mosaics,,POINT(35.27387999609261 0.11199548140904),162,Crop
+163,163,39.911542341177416,-2.5611092030692055,dianafrimpong710@gmail.com,false,2023-03-01 20:38,16.1 secs,Planet Monthly Mosaics,,POINT(39.911542341177416 -2.561109203069206),163,Non-crop
+164,164,35.185079473396854,0.5617606376305225,dianafrimpong710@gmail.com,false,2023-03-01 20:39,44.7 secs,Planet Monthly Mosaics,,POINT(35.185079473396854 0.561760637630522),164,Crop
+165,165,35.05688885441114,-0.6795407745893438,adadebay@umd.edu,false,2023-03-01 20:59,7.0 secs,Mapbox Satellite,,POINT(35.05688885441114 -0.679540774589344),165,Crop
+166,166,37.58793262053442,3.7690288499729587,dianafrimpong710@gmail.com,false,2023-03-01 20:40,20.6 secs,Planet Monthly Mosaics,,POINT(37.58793262053442 3.769028849972959),166,Non-crop
+167,167,34.45875563125551,0.6272795031255634,dianafrimpong710@gmail.com,false,2023-03-01 20:41,64.2 secs,Sentinel-2,,POINT(34.45875563125551 0.627279503125563),167,Crop
+168,168,34.40926954593081,0.2551783003635253,adadebay@umd.edu,false,2023-03-02 22:03,90193.0 secs,Planet Monthly Mosaics,,POINT(34.40926954593081 0.255178300363525),168,Crop
+169,169,39.660802154481644,-3.190642695064495,dianafrimpong710@gmail.com,false,2023-03-01 20:43,25.3 secs,Planet Monthly Mosaics,,POINT(39.660802154481644 -3.190642695064495),169,Non-crop
+170,170,34.54046798139366,0.6859414994541169,dianafrimpong710@gmail.com,false,2023-03-01 20:43,44.0 secs,Planet Monthly Mosaics,,POINT(34.54046798139366 0.685941499454117),170,Crop
+171,171,39.073614246897385,-0.2633723289888229,dianafrimpong710@gmail.com,false,2023-03-01 20:43,8.5 secs,Planet Monthly Mosaics,,POINT(39.073614246897385 -0.263372328988823),171,Non-crop
+172,172,38.432919130857414,1.7396830042557871,dianafrimpong710@gmail.com,false,2023-03-01 20:44,15.8 secs,Planet Monthly Mosaics,,POINT(38.432919130857414 1.739683004255787),172,Non-crop
+173,173,37.095575381750336,-1.0096069277601003,dianafrimpong710@gmail.com,false,2023-03-01 20:44,7.5 secs,Planet Monthly Mosaics,,POINT(37.095575381750336 -1.0096069277601),173,Crop
+174,174,36.65947410135244,-1.1671998161925197,dianafrimpong710@gmail.com,false,2023-03-01 20:45,72.5 secs,Sentinel-2,,POINT(36.65947410135244 -1.16719981619252),174,Crop
+175,175,34.45338777219258,-0.7355236000562643,dianafrimpong710@gmail.com,false,2023-03-01 20:46,29.5 secs,Planet Monthly Mosaics,,POINT(34.45338777219258 -0.735523600056264),175,Non-crop
+176,176,35.91774080987921,-1.0793531043719524,dianafrimpong710@gmail.com,false,2023-03-01 20:47,60.6 secs,Planet Monthly Mosaics,,POINT(35.91774080987921 -1.079353104371952),176,Crop
+177,177,34.72286953140142,0.4356879558642015,dianafrimpong710@gmail.com,false,2023-03-01 20:47,24.7 secs,Planet Monthly Mosaics,,POINT(34.72286953140142 0.435687955864202),177,Non-crop
+178,178,35.247612435415604,1.055323072725927,dianafrimpong710@gmail.com,false,2023-03-01 20:47,27.2 secs,Planet Monthly Mosaics,,POINT(35.247612435415604 1.055323072725927),178,Crop
+179,179,34.17397768556119,0.41298920794555216,adadebay@umd.edu,false,2023-03-02 22:03,21.8 secs,Mapbox Satellite,,POINT(34.17397768556119 0.412989207945552),179,Non-crop
+180,180,36.90739764160443,-0.6045908967298282,dianafrimpong710@gmail.com,false,2023-03-01 20:50,9.2 secs,Planet Monthly Mosaics,,POINT(36.90739764160443 -0.604590896729828),180,Non-crop
+181,181,35.09232298964106,-0.24405121259209608,dianafrimpong710@gmail.com,false,2023-03-01 20:50,23.1 secs,Planet Monthly Mosaics,,POINT(35.09232298964106 -0.244051212592096),181,Crop
+182,182,34.93320904860172,3.089392923071744,dianafrimpong710@gmail.com,false,2023-03-01 20:50,7.0 secs,Planet Monthly Mosaics,,POINT(34.93320904860172 3.089392923071744),182,Non-crop
+183,183,35.386016558505396,0.7061819098528996,dianafrimpong710@gmail.com,false,2023-03-01 20:50,11.0 secs,Planet Monthly Mosaics,,POINT(35.386016558505396 0.7061819098529),183,Crop
+184,184,35.44600492019322,0.6737515821752349,dianafrimpong710@gmail.com,false,2023-03-01 20:51,46.5 secs,Planet Monthly Mosaics,,POINT(35.44600492019322 0.673751582175235),184,Crop
+185,185,36.83715746471805,-0.3040771550035826,dianafrimpong710@gmail.com,false,2023-03-01 20:52,20.8 secs,Planet Monthly Mosaics,,POINT(36.83715746471805 -0.304077155003583),185,Crop
+186,186,35.47235817550058,-1.7747537021443485,dianafrimpong710@gmail.com,false,2023-03-01 20:52,7.2 secs,Planet Monthly Mosaics,,POINT(35.47235817550058 -1.774753702144348),186,Non-crop
+187,187,35.9969624367669,-0.05057581718672911,dianafrimpong710@gmail.com,false,2023-03-01 20:52,16.7 secs,Planet Monthly Mosaics,,POINT(35.9969624367669 -0.050575817186729),187,Crop
+188,188,35.02138764947741,0.9068522061088463,dianafrimpong710@gmail.com,false,2023-03-01 20:53,35.2 secs,Planet Monthly Mosaics,,POINT(35.02138764947741 0.906852206108846),188,Non-crop
+189,189,37.28290916070205,-0.5670627725341534,adadebay@umd.edu,false,2023-03-02 22:06,158.6 secs,Mapbox Satellite,,POINT(37.28290916070205 -0.567062772534153),189,Non-crop
+190,190,36.27531089330441,1.6312781626526536,dianafrimpong710@gmail.com,false,2023-03-01 20:55,20.5 secs,Planet Monthly Mosaics,,POINT(36.27531089330441 1.631278162652654),190,Non-crop
+191,191,38.18613982888146,2.3095627412092896,dianafrimpong710@gmail.com,false,2023-03-01 20:55,10.1 secs,Planet Monthly Mosaics,,POINT(38.18613982888146 2.30956274120929),191,Non-crop
+192,192,38.82823907086845,2.961020075278439,dianafrimpong710@gmail.com,false,2023-03-01 20:56,22.3 secs,Planet Monthly Mosaics,,POINT(38.82823907086845 2.961020075278439),192,Non-crop
+193,193,35.756354105431015,-0.25214860600974354,dianafrimpong710@gmail.com,false,2023-03-01 20:56,26.0 secs,Planet Monthly Mosaics,,POINT(35.756354105431015 -0.252148606009744),193,Crop
+194,194,34.05911892769959,-0.7668905312894166,dianafrimpong710@gmail.com,false,2023-03-01 20:56,8.5 secs,Planet Monthly Mosaics,,POINT(34.05911892769959 -0.766890531289417),194,Non-crop
+195,195,37.434398228197466,-1.4124646108398355,adadebay@umd.edu,false,2023-03-02 22:06,29.6 secs,Planet Monthly Mosaics,,POINT(37.434398228197466 -1.412464610839836),195,Crop
+196,196,34.24813006085403,-0.18754586249713845,dianafrimpong710@gmail.com,false,2023-03-01 20:58,15.6 secs,Planet Monthly Mosaics,,POINT(34.24813006085403 -0.187545862497138),196,Non-crop
+197,197,39.38163103579628,-0.5870464083569815,dianafrimpong710@gmail.com,false,2023-03-01 20:58,6.9 secs,Planet Monthly Mosaics,,POINT(39.38163103579628 -0.587046408356982),197,Non-crop
+198,198,34.407612059311866,4.020736333189546,dianafrimpong710@gmail.com,false,2023-03-01 20:58,6.4 secs,Planet Monthly Mosaics,,POINT(34.407612059311866 4.020736333189546),198,Non-crop
+199,199,34.5106962080211,-0.1159075048798257,dianafrimpong710@gmail.com,false,2023-03-01 20:59,77.3 secs,Planet Monthly Mosaics,,POINT(34.5106962080211 -0.115907504879826),199,Crop
+200,200,39.699070498363014,0.8268795354015316,dianafrimpong710@gmail.com,false,2023-03-01 20:59,6.5 secs,Planet Monthly Mosaics,,POINT(39.699070498363014 0.826879535401532),200,Non-crop
+201,201,35.26606385102699,-0.8488962562008477,dianafrimpong710@gmail.com,false,2023-03-01 21:01,71.0 secs,Sentinel-2,,POINT(35.26606385102699 -0.848896256200848),201,Non-crop
+202,202,37.00569584470588,-0.4607649473863657,dianafrimpong710@gmail.com,false,2023-03-01 21:02,69.1 secs,Planet Monthly Mosaics,,POINT(37.00569584470588 -0.460764947386366),202,Crop
+203,203,34.681833787046784,-0.0017856800434060153,dianafrimpong710@gmail.com,false,2023-03-01 21:03,98.8 secs,Planet Monthly Mosaics,,POINT(34.681833787046784 -0.001785680043406),203,Crop
+204,204,35.00322143113498,-0.37269709187609834,adadebay@umd.edu,false,2023-03-02 22:07,29.3 secs,Mapbox Satellite,,POINT(35.00322143113498 -0.372697091876098),204,Non-crop
+205,205,35.09439243911183,0.4304058574197333,dianafrimpong710@gmail.com,false,2023-03-01 21:06,47.2 secs,Planet Monthly Mosaics,,POINT(35.09439243911183 0.430405857419733),205,Crop
+206,206,35.13586471270375,3.8666126635096743,dianafrimpong710@gmail.com,false,2023-03-01 21:06,13.0 secs,Planet Monthly Mosaics,,POINT(35.13586471270375 3.866612663509674),206,Non-crop
+207,207,38.15212804664146,3.3677157950076575,dianafrimpong710@gmail.com,false,2023-03-01 21:06,8.9 secs,Planet Monthly Mosaics,,POINT(38.15212804664146 3.367715795007658),207,Non-crop
+208,208,38.286578480177624,-3.580862928287623,dianafrimpong710@gmail.com,false,2023-03-01 21:09,156.5 secs,Planet Monthly Mosaics,,POINT(38.286578480177624 -3.580862928287623),208,Non-crop
+209,209,39.404093137783995,2.45152108353291,dianafrimpong710@gmail.com,false,2023-03-01 21:09,22.4 secs,Planet Monthly Mosaics,,POINT(39.404093137783995 2.45152108353291),209,Non-crop
+210,210,35.065711700959476,2.547656684794862,dianafrimpong710@gmail.com,false,2023-03-01 21:09,7.5 secs,Planet Monthly Mosaics,,POINT(35.065711700959476 2.547656684794862),210,Non-crop
+211,211,35.947769272904495,0.20415397808837918,dianafrimpong710@gmail.com,false,2023-03-01 21:10,46.9 secs,Planet Monthly Mosaics,,POINT(35.947769272904495 0.204153978088379),211,Non-crop
+212,212,37.91801786296307,-2.1375884435327785,dianafrimpong710@gmail.com,false,2023-03-01 21:12,107.0 secs,Planet Monthly Mosaics,,POINT(37.91801786296307 -2.137588443532778),212,Non-crop
+213,213,36.88669703425683,0.42706564857485335,dianafrimpong710@gmail.com,false,2023-03-01 21:12,17.0 secs,Planet Monthly Mosaics,,POINT(36.88669703425683 0.427065648574853),213,Non-crop
+214,214,34.068160039789994,-0.6537499847545581,dianafrimpong710@gmail.com,false,2023-03-01 21:12,19.7 secs,Planet Monthly Mosaics,,POINT(34.068160039789994 -0.653749984754558),214,Crop
+215,215,37.99915424752388,0.06625355062429386,dianafrimpong710@gmail.com,false,2023-03-01 21:15,136.3 secs,Planet Monthly Mosaics,,POINT(37.99915424752388 0.066253550624294),215,Non-crop
+216,216,38.23840190779963,1.666609091145675,dianafrimpong710@gmail.com,false,2023-03-01 21:15,13.1 secs,Planet Monthly Mosaics,,POINT(38.23840190779963 1.666609091145675),216,Non-crop
+217,217,38.09419036742432,2.9609319691321545,dianafrimpong710@gmail.com,false,2023-03-01 21:15,15.5 secs,Planet Monthly Mosaics,,POINT(38.09419036742432 2.960931969132154),217,Non-crop
+218,218,40.11268741129131,0.07066709430285514,dianafrimpong710@gmail.com,false,2023-03-01 21:15,6.9 secs,Planet Monthly Mosaics,,POINT(40.11268741129131 0.070667094302855),218,Non-crop
+219,219,35.33297997639196,-0.8617340640007177,dianafrimpong710@gmail.com,false,2023-03-01 21:16,33.4 secs,Planet Monthly Mosaics,,POINT(35.33297997639196 -0.861734064000718),219,Crop
+220,220,35.85807814705425,4.10000582662628,dianafrimpong710@gmail.com,false,2023-03-01 21:16,6.9 secs,Planet Monthly Mosaics,,POINT(35.85807814705425 4.10000582662628),220,Non-crop
+221,221,34.5729975776531,-0.6466759300640075,dianafrimpong710@gmail.com,false,2023-03-01 21:16,7.1 secs,Planet Monthly Mosaics,,POINT(34.5729975776531 -0.646675930064008),221,Crop
+222,222,40.546244593875265,1.1821031768260064,dianafrimpong710@gmail.com,false,2023-03-01 21:16,5.5 secs,Planet Monthly Mosaics,,POINT(40.546244593875265 1.182103176826006),222,Non-crop
+223,223,37.548413950439276,-0.41160735763497225,dianafrimpong710@gmail.com,false,2023-03-01 21:19,146.9 secs,Planet Monthly Mosaics,,POINT(37.548413950439276 -0.411607357634972),223,Non-crop
+224,224,35.75968549281819,-0.9621064827168123,dianafrimpong710@gmail.com,false,2023-03-01 21:19,15.7 secs,Planet Monthly Mosaics,,POINT(35.75968549281819 -0.962106482716812),224,Crop
+225,225,35.074744972874576,-0.7925917814780629,dianafrimpong710@gmail.com,false,2023-03-01 21:19,30.6 secs,Planet Monthly Mosaics,,POINT(35.074744972874576 -0.792591781478063),225,Crop
+226,226,34.46024019008391,3.53618931300853,dianafrimpong710@gmail.com,false,2023-03-01 21:20,6.3 secs,Planet Monthly Mosaics,,POINT(34.46024019008391 3.53618931300853),226,Non-crop
+227,227,38.83677570935286,2.824285402842024,dianafrimpong710@gmail.com,false,2023-03-01 21:20,6.1 secs,Planet Monthly Mosaics,,POINT(38.83677570935286 2.824285402842024),227,Non-crop
+228,228,37.397368184916004,-0.6140394095524093,dianafrimpong710@gmail.com,false,2023-03-01 21:20,26.3 secs,Planet Monthly Mosaics,,POINT(37.397368184916004 -0.614039409552409),228,Crop
+229,229,35.762616150651965,-1.8264944601622872,dianafrimpong710@gmail.com,false,2023-03-01 21:20,18.8 secs,Planet Monthly Mosaics,,POINT(35.762616150651965 -1.826494460162287),229,Non-crop
+230,230,36.54848043845705,-1.000772776636237,dianafrimpong710@gmail.com,false,2023-03-01 21:22,72.8 secs,Sentinel-2,,POINT(36.54848043845705 -1.000772776636237),230,Crop
+231,231,36.02608988189172,3.627360664812145,dianafrimpong710@gmail.com,false,2023-03-01 21:22,18.8 secs,Planet Monthly Mosaics,,POINT(36.02608988189172 3.627360664812145),231,Non-crop
+232,232,33.98152361411099,-0.47111917194585895,dianafrimpong710@gmail.com,false,2023-03-01 21:23,42.5 secs,Planet Monthly Mosaics,,POINT(33.98152361411099 -0.471119171945859),232,Non-crop
+233,233,36.08813405957186,-0.941069865990557,dianafrimpong710@gmail.com,false,2023-03-01 21:23,12.4 secs,Planet Monthly Mosaics,,POINT(36.08813405957186 -0.941069865990557),233,Crop
+234,234,37.60598532487981,-0.717185806924993,dianafrimpong710@gmail.com,false,2023-03-01 21:24,40.2 secs,Planet Monthly Mosaics,,POINT(37.60598532487981 -0.717185806924993),234,Non-crop
+235,235,34.46747779967232,0.8305382197080086,dianafrimpong710@gmail.com,false,2023-03-01 21:24,35.8 secs,Planet Monthly Mosaics,,POINT(34.46747779967232 0.830538219708009),235,Crop
+236,236,34.840773987663475,-0.36183332301270066,dianafrimpong710@gmail.com,false,2023-03-01 21:25,28.0 secs,Planet Monthly Mosaics,,POINT(34.840773987663475 -0.361833323012701),236,Non-crop
+237,237,37.41092767037453,-2.625588354308176,dianafrimpong710@gmail.com,false,2023-03-01 21:25,5.5 secs,Planet Monthly Mosaics,,POINT(37.41092767037453 -2.625588354308176),237,Non-crop
+238,238,34.5067667042273,4.001073110839722,dianafrimpong710@gmail.com,false,2023-03-01 21:25,6.0 secs,Planet Monthly Mosaics,,POINT(34.5067667042273 4.001073110839722),238,Non-crop
+239,239,37.67487855659351,-0.49020904050426295,dianafrimpong710@gmail.com,false,2023-03-01 21:25,18.9 secs,Planet Monthly Mosaics,,POINT(37.67487855659351 -0.490209040504263),239,Crop
+240,240,36.81888775078636,1.9849372691074236,dianafrimpong710@gmail.com,false,2023-03-01 21:25,6.7 secs,Planet Monthly Mosaics,,POINT(36.81888775078636 1.984937269107424),240,Non-crop
+241,241,35.24994048248167,-1.0513444774351794,dianafrimpong710@gmail.com,false,2023-03-01 21:25,10.6 secs,Planet Monthly Mosaics,,POINT(35.24994048248167 -1.051344477435179),241,Crop
+242,242,36.38636671973139,-0.5930179628924186,dianafrimpong710@gmail.com,false,2023-03-01 21:26,15.0 secs,Planet Monthly Mosaics,,POINT(36.38636671973139 -0.593017962892419),242,Non-crop
+243,243,40.907140441399896,0.6303587257539014,dianafrimpong710@gmail.com,false,2023-03-01 21:26,15.0 secs,Planet Monthly Mosaics,,POINT(40.907140441399896 0.630358725753901),243,Non-crop
+244,244,34.96987624938218,1.1655669652004106,dianafrimpong710@gmail.com,false,2023-03-01 21:26,12.3 secs,Planet Monthly Mosaics,,POINT(34.96987624938218 1.165566965200411),244,Crop
+245,245,35.35634367392324,1.4582707365296375,dianafrimpong710@gmail.com,false,2023-03-01 21:26,12.9 secs,Planet Monthly Mosaics,,POINT(35.35634367392324 1.458270736529638),245,Non-crop
+246,246,40.546097997935725,-0.07612710307294997,dianafrimpong710@gmail.com,false,2023-03-01 21:26,5.6 secs,Planet Monthly Mosaics,,POINT(40.546097997935725 -0.07612710307295),246,Non-crop
+247,247,37.16390525162651,-0.7420742630040361,dianafrimpong710@gmail.com,false,2023-03-01 21:27,22.3 secs,Planet Monthly Mosaics,,POINT(37.16390525162651 -0.742074263004036),247,Crop
+248,248,34.45068841495048,0.2338980943932806,dianafrimpong710@gmail.com,false,2023-03-01 21:27,20.6 secs,Planet Monthly Mosaics,,POINT(34.45068841495048 0.233898094393281),248,Crop
+249,249,37.498286295079225,0.9862352131017155,dianafrimpong710@gmail.com,false,2023-03-01 21:27,16.0 secs,Planet Monthly Mosaics,,POINT(37.498286295079225 0.986235213101716),249,Non-crop
+250,250,34.73415295793003,0.018438781875223693,adadebay@umd.edu,false,2023-03-02 22:07,6.4 secs,Mapbox Satellite,,POINT(34.73415295793003 0.018438781875224),250,Non-crop
+251,251,36.85858969388467,-0.4686485504993926,adadebay@umd.edu,false,2023-03-02 22:07,8.6 secs,Mapbox Satellite,,POINT(36.85858969388467 -0.468648550499393),251,Crop
+252,252,35.26890826320225,-0.6564145540663221,adadebay@umd.edu,false,2023-03-02 22:07,5.8 secs,Mapbox Satellite,,POINT(35.26890826320225 -0.656414554066322),252,Crop
+253,253,40.525409290323495,2.6319993375838955,adadebay@umd.edu,false,2023-03-02 22:07,4.9 secs,Mapbox Satellite,,POINT(40.525409290323495 2.631999337583896),253,Non-crop
+254,254,34.92265893447306,0.5353286427591524,adadebay@umd.edu,false,2023-03-02 22:07,5.6 secs,Mapbox Satellite,,POINT(34.92265893447306 0.535328642759152),254,Non-crop
+255,255,36.90455311386702,1.1977744701246156,adadebay@umd.edu,false,2023-03-02 22:07,4.7 secs,Mapbox Satellite,,POINT(36.90455311386702 1.197774470124616),255,Non-crop
+256,256,34.5180586260015,0.7789264000463355,adadebay@umd.edu,false,2023-03-02 22:08,16.2 secs,Mapbox Satellite,,POINT(34.5180586260015 0.778926400046336),256,Non-crop
+257,257,36.058209274389014,-1.3463982887488246,adadebay@umd.edu,false,2023-03-02 22:08,5.3 secs,Mapbox Satellite,,POINT(36.058209274389014 -1.346398288748825),257,Non-crop
+258,258,39.395468711706776,2.0383453012130155,adadebay@umd.edu,false,2023-03-02 22:08,4.9 secs,Mapbox Satellite,,POINT(39.395468711706776 2.038345301213016),258,Non-crop
+259,259,39.05025716334389,1.2727642254608016,adadebay@umd.edu,false,2023-03-02 22:08,5.3 secs,Mapbox Satellite,,POINT(39.05025716334389 1.272764225460802),259,Non-crop
+260,260,40.78878641079977,0.947351946589384,adadebay@umd.edu,false,2023-03-02 22:08,4.7 secs,Mapbox Satellite,,POINT(40.78878641079977 0.947351946589384),260,Non-crop
+261,261,39.46290672282505,-0.013057674497699916,adadebay@umd.edu,false,2023-03-02 22:08,5.0 secs,Mapbox Satellite,,POINT(39.46290672282505 -0.0130576744977),261,Non-crop
+262,262,35.19685108842834,3.9040331699588258,adadebay@umd.edu,false,2023-03-02 22:08,11.5 secs,Mapbox Satellite,,POINT(35.19685108842834 3.904033169958826),262,Non-crop
+263,263,37.299210366889746,-0.7882590697784982,adadebay@umd.edu,false,2023-03-03 17:29,23.2 secs,Mapbox Satellite,,POINT(37.299210366889746 -0.788259069778498),263,Non-crop
+264,264,35.134539162750926,1.0904367967402915,adadebay@umd.edu,false,2023-03-03 17:30,24.2 secs,Mapbox Satellite,,POINT(35.134539162750926 1.090436796740292),264,Crop
+265,265,38.19300016102455,-1.6485412210153514,adadebay@umd.edu,false,2023-03-03 17:51,1271.7 secs,Planet Monthly Mosaics,,POINT(38.19300016102455 -1.648541221015351),265,Non-crop
+266,266,37.73860020267111,-3.331557513616808,adadebay@umd.edu,false,2023-03-03 17:51,12.5 secs,Mapbox Satellite,,POINT(37.73860020267111 -3.331557513616808),266,Non-crop
+267,267,36.183154340115976,2.7001405183904446,adadebay@umd.edu,false,2023-03-03 17:51,5.2 secs,Mapbox Satellite,,POINT(36.183154340115976 2.700140518390445),267,Non-crop
+268,268,39.9003149820409,3.4102972857651417,adadebay@umd.edu,false,2023-03-03 17:51,5.0 secs,Mapbox Satellite,,POINT(39.9003149820409 3.410297285765142),268,Non-crop
+269,269,36.89256033951802,-1.198551976200557,adadebay@umd.edu,false,2023-03-03 17:51,10.6 secs,Mapbox Satellite,,POINT(36.89256033951802 -1.198551976200557),269,Non-crop
+270,270,34.111742992464094,-0.03294774771910352,adadebay@umd.edu,false,2023-03-03 17:51,8.1 secs,Mapbox Satellite,,POINT(34.111742992464094 -0.032947747719104),270,Non-crop
+271,271,38.55271191716017,-1.5870930361786544,adadebay@umd.edu,false,2023-03-03 17:52,4.8 secs,Mapbox Satellite,,POINT(38.55271191716017 -1.587093036178654),271,Non-crop
+272,272,35.79147363846596,1.8930771879554555,adadebay@umd.edu,false,2023-03-03 17:52,9.3 secs,Mapbox Satellite,,POINT(35.79147363846596 1.893077187955456),272,Non-crop
+273,273,35.0802486455078,0.7325502982864748,adadebay@umd.edu,false,2023-03-03 17:52,12.5 secs,Mapbox Satellite,,POINT(35.0802486455078 0.732550298286475),273,Crop
+274,274,36.66787862075995,-0.3370048147857069,adadebay@umd.edu,false,2023-03-03 17:52,7.4 secs,Mapbox Satellite,,POINT(36.66787862075995 -0.337004814785707),274,Non-crop
+275,275,35.382128847748994,-0.8625910503550166,adadebay@umd.edu,false,2023-03-03 17:53,32.1 secs,Mapbox Satellite,,POINT(35.382128847748994 -0.862591050355017),275,Non-crop
+276,276,37.34840090595225,0.23193127521008539,adadebay@umd.edu,false,2023-03-03 17:53,5.1 secs,Mapbox Satellite,,POINT(37.34840090595225 0.231931275210085),276,Non-crop
+277,277,37.52952524833004,-2.724401129435582,adadebay@umd.edu,false,2023-03-03 17:53,5.8 secs,Mapbox Satellite,,POINT(37.52952524833004 -2.724401129435582),277,Non-crop
+278,278,34.30386139976155,0.2951159530038938,adadebay@umd.edu,false,2023-03-03 17:53,35.7 secs,Mapbox Satellite,,POINT(34.30386139976155 0.295115953003894),278,Non-crop
+279,279,35.664840363056655,1.690372204559844,dianafrimpong710@gmail.com,false,2023-03-03 21:31,41.4 secs,Planet Monthly Mosaics,,POINT(35.664840363056655 1.690372204559844),279,Non-crop
+280,280,39.19739507190026,-1.2216252242734178,dianafrimpong710@gmail.com,false,2023-03-03 21:32,21.0 secs,Planet Monthly Mosaics,,POINT(39.19739507190026 -1.221625224273418),280,Non-crop
+281,281,35.184928340727154,1.8744824920807754,dianafrimpong710@gmail.com,false,2023-03-03 21:32,22.3 secs,Planet Monthly Mosaics,,POINT(35.184928340727154 1.874482492080775),281,Non-crop
+282,282,37.78857700274938,-3.3771286904231905,dianafrimpong710@gmail.com,false,2023-03-03 21:33,54.1 secs,Planet Monthly Mosaics,,POINT(37.78857700274938 -3.37712869042319),282,Non-crop
+283,283,36.68502729602952,2.514325852545885,dianafrimpong710@gmail.com,false,2023-03-03 21:33,22.1 secs,Planet Monthly Mosaics,,POINT(36.68502729602952 2.514325852545885),283,Non-crop
+284,284,35.237040051019214,1.6524385569289506,dianafrimpong710@gmail.com,false,2023-03-03 21:34,55.6 secs,Planet Monthly Mosaics,,POINT(35.237040051019214 1.652438556928951),284,Crop
+285,285,36.53526300735865,-0.4683613786490718,dianafrimpong710@gmail.com,false,2023-03-03 21:35,60.9 secs,Planet Monthly Mosaics,,POINT(36.53526300735865 -0.468361378649072),285,Crop
+286,286,38.0760525856689,-0.11913545569567449,dianafrimpong710@gmail.com,false,2023-03-03 21:37,85.5 secs,Planet Monthly Mosaics,,POINT(38.0760525856689 -0.119135455695674),286,Non-crop
+287,287,34.35644028067466,0.5608866070711257,dianafrimpong710@gmail.com,false,2023-03-03 21:37,29.1 secs,Planet Monthly Mosaics,,POINT(34.35644028067466 0.560886607071126),287,Crop
+288,288,38.65484794845974,-0.4860956688299213,dianafrimpong710@gmail.com,false,2023-03-03 21:38,36.8 secs,Planet Monthly Mosaics,,POINT(38.65484794845974 -0.486095668829921),288,Non-crop
+289,289,35.93734608846335,-1.057140931452576,dianafrimpong710@gmail.com,false,2023-03-03 21:38,30.5 secs,Planet Monthly Mosaics,,POINT(35.93734608846335 -1.057140931452576),289,Non-crop
+290,290,37.80582391504641,3.8350499412603556,dianafrimpong710@gmail.com,false,2023-03-03 21:39,25.1 secs,Planet Monthly Mosaics,,POINT(37.80582391504641 3.835049941260356),290,Non-crop
+291,291,35.25737937467433,-0.8666069188032641,dianafrimpong710@gmail.com,false,2023-03-03 21:39,35.5 secs,Planet Monthly Mosaics,,POINT(35.25737937467433 -0.866606918803264),291,Non-crop
+292,292,34.35788383131346,0.19694288433069554,dianafrimpong710@gmail.com,false,2023-03-03 21:45,345.2 secs,Planet Monthly Mosaics,,POINT(34.35788383131346 0.196942884330696),292,Crop
+293,293,34.818940898978965,-1.2688823327413155,dianafrimpong710@gmail.com,false,2023-03-03 21:45,22.4 secs,Planet Monthly Mosaics,,POINT(34.818940898978965 -1.268882332741316),293,Non-crop
+294,294,34.93880589858098,2.3229353279690117,dianafrimpong710@gmail.com,false,2023-03-03 21:45,10.0 secs,Planet Monthly Mosaics,,POINT(34.93880589858098 2.322935327969012),294,Non-crop
+295,295,39.054542526331986,0.0026259847551115003,dianafrimpong710@gmail.com,false,2023-03-03 21:46,11.7 secs,Planet Monthly Mosaics,,POINT(39.054542526331986 0.002625984755112),295,Non-crop
+296,296,36.07763478509327,-1.1708826042522547,dianafrimpong710@gmail.com,false,2023-03-03 21:47,98.0 secs,Planet Monthly Mosaics,,POINT(36.07763478509327 -1.170882604252255),296,Crop
+297,297,34.90669887109912,0.7293370301766702,dianafrimpong710@gmail.com,false,2023-03-03 21:53,375.6 secs,Planet Monthly Mosaics,,POINT(34.90669887109912 0.72933703017667),297,Crop
+298,298,38.79853398079912,0.9642160077813193,dianafrimpong710@gmail.com,false,2023-03-03 21:54,10.7 secs,Planet Monthly Mosaics,,POINT(38.79853398079912 0.964216007781319),298,Non-crop
+299,299,37.553857774558885,-2.9026633735747365,dianafrimpong710@gmail.com,false,2023-03-03 21:54,36.8 secs,Planet Monthly Mosaics,,POINT(37.553857774558885 -2.902663373574736),299,Crop
+300,300,38.01347608722678,2.900570657102886,dianafrimpong710@gmail.com,false,2023-03-03 21:55,40.1 secs,Planet Monthly Mosaics,,POINT(38.01347608722678 2.900570657102886),300,Non-crop
+301,301,37.132692772610476,0.39776039039835437,dianafrimpong710@gmail.com,false,2023-03-03 21:55,19.2 secs,Planet Monthly Mosaics,,POINT(37.132692772610476 0.397760390398354),301,Non-crop
+302,302,37.9392642060917,2.9892719733950126,dianafrimpong710@gmail.com,false,2023-03-03 21:55,9.9 secs,Planet Monthly Mosaics,,POINT(37.9392642060917 2.989271973395013),302,Non-crop
+303,303,34.51506919888127,0.1202453794900851,dianafrimpong710@gmail.com,false,2023-03-03 21:55,9.8 secs,Planet Monthly Mosaics,,POINT(34.51506919888127 0.120245379490085),303,Crop
+304,304,39.63767229599631,-0.12049561541535113,dianafrimpong710@gmail.com,false,2023-03-03 21:56,10.8 secs,Planet Monthly Mosaics,,POINT(39.63767229599631 -0.120495615415351),304,Non-crop
+305,305,40.485326212115005,3.093570121762987,dianafrimpong710@gmail.com,false,2023-03-03 21:56,9.1 secs,Planet Monthly Mosaics,,POINT(40.485326212115005 3.093570121762987),305,Non-crop
+306,306,36.85239673523007,-0.05853659594733882,dianafrimpong710@gmail.com,false,2023-03-03 21:56,22.1 secs,Planet Monthly Mosaics,,POINT(36.85239673523007 -0.058536595947339),306,Non-crop
+307,307,37.5763483597854,-0.9823710756791123,dianafrimpong710@gmail.com,false,2023-03-03 21:57,30.6 secs,Planet Monthly Mosaics,,POINT(37.5763483597854 -0.982371075679112),307,Non-crop
+308,308,39.55791935681194,1.5349026436085218,dianafrimpong710@gmail.com,false,2023-03-03 21:57,11.2 secs,Planet Monthly Mosaics,,POINT(39.55791935681194 1.534902643608522),308,Non-crop
+309,309,37.699949750619936,0.811948379073906,dianafrimpong710@gmail.com,false,2023-03-03 22:08,682.9 secs,Planet Monthly Mosaics,,POINT(37.699949750619936 0.811948379073906),309,Non-crop
+310,310,37.61422072618653,-2.0872413462062473,adadebay@umd.edu,false,2023-03-06 13:02,11.2 secs,Mapbox Satellite,,POINT(37.61422072618653 -2.087241346206247),310,Non-crop
+311,311,41.07513171921359,3.1888676288992936,adadebay@umd.edu,false,2023-03-06 13:02,5.1 secs,Mapbox Satellite,,POINT(41.07513171921359 3.188867628899294),311,Non-crop
+312,312,38.15815408847578,-0.8111453505853832,adadebay@umd.edu,false,2023-03-06 13:02,6.5 secs,Mapbox Satellite,,POINT(38.15815408847578 -0.811145350585383),312,Non-crop
+313,313,34.736270179275344,-0.5624127830726717,adadebay@umd.edu,false,2023-03-06 13:02,6.3 secs,Mapbox Satellite,,POINT(34.736270179275344 -0.562412783072672),313,Crop
+314,314,35.375810528396606,-0.21120699908951038,adadebay@umd.edu,false,2023-03-06 13:03,8.4 secs,Mapbox Satellite,,POINT(35.375810528396606 -0.21120699908951),314,Crop
+315,315,37.21904951713753,-0.7068700226033653,adadebay@umd.edu,false,2023-03-06 13:03,7.0 secs,Mapbox Satellite,,POINT(37.21904951713753 -0.706870022603365),315,Non-crop
+316,316,39.83400661063378,-1.68890740390545,adadebay@umd.edu,false,2023-03-06 13:03,4.7 secs,Mapbox Satellite,,POINT(39.83400661063378 -1.68890740390545),316,Non-crop
+317,317,34.191058880953285,-0.9215307463490227,adadebay@umd.edu,false,2023-03-06 13:03,5.5 secs,Mapbox Satellite,,POINT(34.191058880953285 -0.921530746349023),317,Crop
+318,318,34.49184864640101,-0.7460763000252552,adadebay@umd.edu,false,2023-03-06 13:03,6.1 secs,Mapbox Satellite,,POINT(34.49184864640101 -0.746076300025255),318,Crop
+319,319,37.651127920351016,-0.6203277536564297,adadebay@umd.edu,false,2023-03-06 13:03,13.7 secs,Mapbox Satellite,,POINT(37.651127920351016 -0.62032775365643),319,Non-crop
+320,320,38.75780790144454,-0.6667358112308936,adadebay@umd.edu,false,2023-03-06 13:04,28.5 secs,Mapbox Satellite,,POINT(38.75780790144454 -0.666735811230894),320,Non-crop
+321,321,34.94783457049091,-0.11246745761080304,adadebay@umd.edu,false,2023-03-06 13:04,19.6 secs,Mapbox Satellite,,POINT(34.94783457049091 -0.112467457610803),321,Non-crop
+322,322,39.229061071429356,-0.666375893413772,adadebay@umd.edu,false,2023-03-06 13:04,5.6 secs,Mapbox Satellite,,POINT(39.229061071429356 -0.666375893413772),322,Non-crop
+323,323,38.88788044369188,-4.24884778504972,adadebay@umd.edu,false,2023-03-06 13:04,6.5 secs,Mapbox Satellite,,POINT(38.88788044369188 -4.24884778504972),323,Non-crop
+324,324,36.71627653401663,2.0002891318295455,adadebay@umd.edu,false,2023-03-06 13:04,7.1 secs,Mapbox Satellite,,POINT(36.71627653401663 2.000289131829546),324,Non-crop
+325,325,35.99051353043517,-1.0041755970886126,adadebay@umd.edu,false,2023-03-06 13:04,5.9 secs,Mapbox Satellite,,POINT(35.99051353043517 -1.004175597088613),325,Non-crop
+326,326,34.693116889906136,0.7036994275098215,adadebay@umd.edu,false,2023-03-06 13:05,6.0 secs,Mapbox Satellite,,POINT(34.693116889906136 0.703699427509822),326,Crop
+327,327,36.80110616930354,-1.3991582831341616,adadebay@umd.edu,false,2023-03-06 13:05,12.0 secs,Mapbox Satellite,,POINT(36.80110616930354 -1.399158283134162),327,Non-crop
+328,328,34.359713263958206,3.7495077872591676,adadebay@umd.edu,false,2023-03-06 13:05,5.3 secs,Mapbox Satellite,,POINT(34.359713263958206 3.749507787259168),328,Non-crop
+329,329,34.89872798832474,3.7711850926712276,adadebay@umd.edu,false,2023-03-06 13:05,5.8 secs,Mapbox Satellite,,POINT(34.89872798832474 3.771185092671228),329,Non-crop
+330,330,34.75823814690865,0.768390378516022,adadebay@umd.edu,false,2023-03-06 13:05,10.5 secs,Mapbox Satellite,,POINT(34.75823814690865 0.768390378516022),330,Non-crop
+331,331,35.03043307620979,-0.435833843994421,adadebay@umd.edu,false,2023-03-06 13:05,6.6 secs,Mapbox Satellite,,POINT(35.03043307620979 -0.435833843994421),331,Crop
+332,332,39.91725992960783,-2.536230581743613,adadebay@umd.edu,false,2023-03-06 13:07,76.8 secs,Sentinel-2,,POINT(39.91725992960783 -2.536230581743613),332,Non-crop
+333,333,38.04110617648035,2.134810668583166,adadebay@umd.edu,false,2023-03-06 13:07,31.1 secs,Mapbox Satellite,,POINT(38.04110617648035 2.134810668583166),333,Non-crop
+334,334,38.616261925366786,2.5167846397007954,adadebay@umd.edu,false,2023-03-06 13:07,6.2 secs,Mapbox Satellite,,POINT(38.616261925366786 2.516784639700795),334,Non-crop
+335,335,36.910665570255915,-1.1618142066887174,adadebay@umd.edu,false,2023-03-06 13:08,77.1 secs,Sentinel-2,,POINT(36.910665570255915 -1.161814206688717),335,Crop
+336,336,36.42768875867901,2.649630024893417,adadebay@umd.edu,false,2023-03-06 13:09,14.0 secs,Sentinel-2,,POINT(36.42768875867901 2.649630024893417),336,Non-crop
+337,337,37.41839115014418,-1.0816838435200997,adadebay@umd.edu,false,2023-03-06 13:10,77.2 secs,Planet Monthly Mosaics,,POINT(37.41839115014418 -1.0816838435201),337,Crop
+338,338,37.10693932494706,-0.6350245887261098,adadebay@umd.edu,false,2023-03-06 13:10,19.2 secs,Mapbox Satellite,,POINT(37.10693932494706 -0.63502458872611),338,Non-crop
+339,339,36.984429661995755,3.311675194970429,adadebay@umd.edu,false,2023-03-06 13:10,5.4 secs,Mapbox Satellite,,POINT(36.984429661995755 3.311675194970429),339,Non-crop
+340,340,36.144412583364996,0.004963373055491912,adadebay@umd.edu,false,2023-03-06 13:11,29.8 secs,Mapbox Satellite,,POINT(36.144412583364996 0.004963373055492),340,Non-crop
+341,341,36.70078650447418,3.309703935432483,adadebay@umd.edu,false,2023-03-06 13:11,5.5 secs,Mapbox Satellite,,POINT(36.70078650447418 3.309703935432483),341,Non-crop
+342,342,38.75161279448816,2.378729348687549,adadebay@umd.edu,false,2023-03-06 13:11,5.9 secs,Mapbox Satellite,,POINT(38.75161279448816 2.378729348687549),342,Non-crop
+343,343,40.06707636849588,3.45275268077823,adadebay@umd.edu,false,2023-03-06 13:11,4.8 secs,Mapbox Satellite,,POINT(40.06707636849588 3.45275268077823),343,Non-crop
+344,344,36.56210500652624,-1.0837246415363992,adadebay@umd.edu,false,2023-03-06 13:11,4.8 secs,Mapbox Satellite,,POINT(36.56210500652624 -1.083724641536399),344,Non-crop
+345,345,34.336041577902456,0.7166491969812425,adadebay@umd.edu,false,2023-03-06 13:12,23.0 secs,Planet Monthly Mosaics,,POINT(34.336041577902456 0.716649196981242),345,Crop
+346,346,39.598853154828326,2.8858817878871452,adadebay@umd.edu,false,2023-03-06 13:12,10.0 secs,Mapbox Satellite,,POINT(39.598853154828326 2.885881787887145),346,Non-crop
+347,347,36.43918846011223,1.440504544935712,adadebay@umd.edu,false,2023-03-06 13:12,5.1 secs,Mapbox Satellite,,POINT(36.43918846011223 1.440504544935712),347,Non-crop
+348,348,35.54210939603061,-1.327991441743508,adadebay@umd.edu,false,2023-03-06 13:12,5.7 secs,Mapbox Satellite,,POINT(35.54210939603061 -1.327991441743508),348,Non-crop
+349,349,34.24661018198986,3.7788091335579623,adadebay@umd.edu,false,2023-03-06 13:12,5.0 secs,Mapbox Satellite,,POINT(34.24661018198986 3.778809133557962),349,Non-crop
+350,350,38.155177833382425,-0.9094747900561307,adadebay@umd.edu,false,2023-03-06 13:12,6.9 secs,Mapbox Satellite,,POINT(38.155177833382425 -0.909474790056131),350,Non-crop
+351,351,37.72392326099627,-2.8587133707172647,adadebay@umd.edu,false,2023-03-06 13:12,7.6 secs,Mapbox Satellite,,POINT(37.72392326099627 -2.858713370717265),351,Non-crop
+352,352,39.185711371300165,0.11214360263653626,adadebay@umd.edu,false,2023-03-06 13:12,5.6 secs,Mapbox Satellite,,POINT(39.185711371300165 0.112143602636536),352,Non-crop
+353,353,36.81517663408579,0.06603508799998077,adadebay@umd.edu,false,2023-03-06 13:13,5.3 secs,Mapbox Satellite,,POINT(36.81517663408579 0.066035087999981),353,Non-crop
+354,354,39.73857946305931,1.3005086068260963,adadebay@umd.edu,false,2023-03-06 13:13,5.9 secs,Mapbox Satellite,,POINT(39.73857946305931 1.300508606826096),354,Non-crop
+355,355,34.89094893452659,0.5432189512967784,adadebay@umd.edu,false,2023-03-06 13:13,6.2 secs,Mapbox Satellite,,POINT(34.89094893452659 0.543218951296778),355,Crop
+356,356,35.34730497147133,1.8974096389644381,adadebay@umd.edu,false,2023-03-06 13:13,5.8 secs,Mapbox Satellite,,POINT(35.34730497147133 1.897409638964438),356,Non-crop
+357,357,39.4560023573566,-3.7589481349304537,adadebay@umd.edu,false,2023-03-06 13:13,4.4 secs,Mapbox Satellite,,POINT(39.4560023573566 -3.758948134930454),357,Non-crop
+358,358,34.19309376608378,-0.7112667390382308,adadebay@umd.edu,false,2023-03-06 13:50,18.0 secs,Mapbox Satellite,,POINT(34.19309376608378 -0.711266739038231),358,Crop
+359,359,38.08520562034121,-0.7104578632257538,adadebay@umd.edu,false,2023-03-06 13:14,28.7 secs,Planet Monthly Mosaics,,POINT(38.08520562034121 -0.710457863225754),359,Non-crop
+360,360,39.8764623690857,-0.41394507470955105,adadebay@umd.edu,false,2023-03-06 13:14,9.7 secs,Planet Monthly Mosaics,,POINT(39.8764623690857 -0.413945074709551),360,Non-crop
+361,361,36.59090129950348,4.401266892103294,adadebay@umd.edu,false,2023-03-06 13:14,8.2 secs,Planet Monthly Mosaics,,POINT(36.59090129950348 4.401266892103294),361,Non-crop
+362,362,36.80582306681952,3.5421413369810377,adadebay@umd.edu,false,2023-03-06 13:15,10.6 secs,Planet Monthly Mosaics,,POINT(36.80582306681952 3.542141336981038),362,Non-crop
+363,363,40.84664060103932,1.7168775775104577,adadebay@umd.edu,false,2023-03-06 13:15,7.0 secs,Planet Monthly Mosaics,,POINT(40.84664060103932 1.716877577510458),363,Non-crop
+364,364,40.68156500663407,1.129225977950579,adadebay@umd.edu,false,2023-03-06 13:15,6.1 secs,Planet Monthly Mosaics,,POINT(40.68156500663407 1.129225977950579),364,Non-crop
+365,365,38.55642953523764,2.918040794846058,adadebay@umd.edu,false,2023-03-06 13:50,5.4 secs,Mapbox Satellite,,POINT(38.55642953523764 2.918040794846058),365,Non-crop
+366,366,39.0709024919474,-3.7913328870442045,adadebay@umd.edu,false,2023-03-06 13:15,8.0 secs,Mapbox Satellite,,POINT(39.0709024919474 -3.791332887044204),366,Non-crop
+367,367,36.638130724991306,-0.49460489559864695,adadebay@umd.edu,false,2023-03-06 13:15,4.6 secs,Mapbox Satellite,,POINT(36.638130724991306 -0.494604895598647),367,Non-crop
+368,368,37.63079766174867,-0.053604990844186254,adadebay@umd.edu,false,2023-03-06 13:15,8.0 secs,Mapbox Satellite,,POINT(37.63079766174867 -0.053604990844186),368,Crop
+369,369,35.827907745167956,-0.853811982262311,adadebay@umd.edu,false,2023-03-06 13:15,4.7 secs,Mapbox Satellite,,POINT(35.827907745167956 -0.853811982262311),369,Non-crop
+370,370,39.167720267265224,1.9374319528339419,adadebay@umd.edu,false,2023-03-06 13:16,4.0 secs,Mapbox Satellite,,POINT(39.167720267265224 1.937431952833942),370,Non-crop
+371,371,34.212762133967715,-0.8357804134194107,adadebay@umd.edu,false,2023-03-06 13:16,5.2 secs,Mapbox Satellite,,POINT(34.212762133967715 -0.835780413419411),371,Crop
+372,372,37.96110516185624,0.14781455235668403,adadebay@umd.edu,false,2023-03-06 13:16,5.6 secs,Mapbox Satellite,,POINT(37.96110516185624 0.147814552356684),372,Non-crop
+373,373,34.47463753982868,0.10990526138305981,adadebay@umd.edu,false,2023-03-06 13:17,80.6 secs,Sentinel-2,,POINT(34.47463753982868 0.10990526138306),373,Crop
+374,374,39.69251931795742,-2.5541267260917877,adadebay@umd.edu,false,2023-03-06 13:17,14.6 secs,Mapbox Satellite,,POINT(39.69251931795742 -2.554126726091788),374,Non-crop
+375,375,36.68244024793016,-1.8676442073681212,adadebay@umd.edu,false,2023-03-06 13:17,6.4 secs,Mapbox Satellite,,POINT(36.68244024793016 -1.867644207368121),375,Non-crop
+376,376,36.853159792048174,3.4206556695885375,adadebay@umd.edu,false,2023-03-06 13:18,6.4 secs,Mapbox Satellite,,POINT(36.853159792048174 3.420655669588538),376,Non-crop
+377,377,38.92586119879842,1.3194550813766501,adadebay@umd.edu,false,2023-03-06 13:18,4.8 secs,Mapbox Satellite,,POINT(38.92586119879842 1.31945508137665),377,Non-crop
+378,378,38.69999902903868,2.7545836053240813,adadebay@umd.edu,false,2023-03-06 13:18,20.7 secs,Mapbox Satellite,,POINT(38.69999902903868 2.754583605324081),378,Non-crop
+379,379,34.83181317134586,1.144867592720978,adadebay@umd.edu,false,2023-03-06 13:18,5.1 secs,Mapbox Satellite,,POINT(34.83181317134586 1.144867592720978),379,Crop
+380,380,40.90693313748294,1.8439313546702625,adadebay@umd.edu,false,2023-03-06 13:18,4.5 secs,Mapbox Satellite,,POINT(40.90693313748294 1.843931354670262),380,Non-crop
+381,381,34.1304218109236,-0.5631023328537231,adadebay@umd.edu,false,2023-03-06 13:18,7.5 secs,Mapbox Satellite,,POINT(34.1304218109236 -0.563102332853723),381,Non-crop
+382,382,39.200957664948234,-2.5959413740347235,adadebay@umd.edu,false,2023-03-06 13:18,6.1 secs,Mapbox Satellite,,POINT(39.200957664948234 -2.595941374034724),382,Non-crop
+383,383,38.63760081316255,1.1224819906657701,adadebay@umd.edu,false,2023-03-06 13:18,5.3 secs,Mapbox Satellite,,POINT(38.63760081316255 1.12248199066577),383,Non-crop
+384,384,34.795026265560566,0.6743079317838352,adadebay@umd.edu,false,2023-03-06 13:19,6.2 secs,Mapbox Satellite,,POINT(34.795026265560566 0.674307931783835),384,Crop
+385,385,40.272953061237764,-1.7216502223117192,adadebay@umd.edu,false,2023-03-06 13:19,5.1 secs,Mapbox Satellite,,POINT(40.272953061237764 -1.721650222311719),385,Non-crop
+386,386,34.511216655298945,-0.40840217196105755,adadebay@umd.edu,false,2023-03-06 13:19,6.7 secs,Mapbox Satellite,,POINT(34.511216655298945 -0.408402171961058),386,Non-crop
+387,387,38.532332486665574,-3.4722325425673475,adadebay@umd.edu,false,2023-03-06 13:19,5.4 secs,Mapbox Satellite,,POINT(38.532332486665574 -3.472232542567348),387,Non-crop
+388,388,34.824113704860466,1.2169539748385623,adadebay@umd.edu,false,2023-03-06 13:19,4.5 secs,Mapbox Satellite,,POINT(34.824113704860466 1.216953974838562),388,Crop
+389,389,37.388102475893305,-0.7540557617057277,adadebay@umd.edu,false,2023-03-06 13:19,8.1 secs,Mapbox Satellite,,POINT(37.388102475893305 -0.754055761705728),389,Crop
+390,390,37.42891460399352,-0.45483461285550036,adadebay@umd.edu,false,2023-03-06 13:19,8.2 secs,Mapbox Satellite,,POINT(37.42891460399352 -0.4548346128555),390,Non-crop
+391,391,36.967887534726245,-0.8095488907087274,adadebay@umd.edu,false,2023-03-06 13:19,5.2 secs,Mapbox Satellite,,POINT(36.967887534726245 -0.809548890708727),391,Non-crop
+392,392,35.527048399962815,4.0989025202231,adadebay@umd.edu,false,2023-03-06 13:19,6.2 secs,Mapbox Satellite,,POINT(35.527048399962815 4.0989025202231),392,Non-crop
+393,393,36.924922755405284,-1.1232711340297845,adadebay@umd.edu,false,2023-03-06 13:20,11.5 secs,Mapbox Satellite,,POINT(36.924922755405284 -1.123271134029784),393,Non-crop
+394,394,37.257050079664545,0.7989869091198432,adadebay@umd.edu,false,2023-03-06 13:20,5.1 secs,Mapbox Satellite,,POINT(37.257050079664545 0.798986909119843),394,Non-crop
+395,395,38.95454874266865,3.252171003985754,adadebay@umd.edu,false,2023-03-06 13:20,5.2 secs,Mapbox Satellite,,POINT(38.95454874266865 3.252171003985754),395,Non-crop
+396,396,35.341565025437944,-0.29359632555640297,adadebay@umd.edu,false,2023-03-06 13:20,28.5 secs,Mapbox Satellite,,POINT(35.341565025437944 -0.293596325556403),396,Non-crop
+397,397,35.79074795959633,-1.9464192254714852,adadebay@umd.edu,false,2023-03-06 13:20,6.7 secs,Mapbox Satellite,,POINT(35.79074795959633 -1.946419225471485),397,Non-crop
+398,398,34.651917881792194,0.5319953940694888,adadebay@umd.edu,false,2023-03-06 13:20,5.1 secs,Mapbox Satellite,,POINT(34.651917881792194 0.531995394069489),398,Non-crop
+399,399,41.1306620114978,3.1745220367098654,adadebay@umd.edu,false,2023-03-06 13:21,5.2 secs,Mapbox Satellite,,POINT(41.1306620114978 3.174522036709865),399,Non-crop
+400,400,34.4371951436428,-0.7709979914980767,adadebay@umd.edu,false,2023-03-06 13:21,4.8 secs,Mapbox Satellite,,POINT(34.4371951436428 -0.770997991498077),400,Non-crop
+401,401,34.558043775685306,3.125264995688962,adadebay@umd.edu,false,2023-03-06 13:21,5.1 secs,Mapbox Satellite,,POINT(34.558043775685306 3.125264995688962),401,Non-crop
+402,402,37.981040475396064,3.6028567914524987,adadebay@umd.edu,false,2023-03-06 13:21,5.6 secs,Mapbox Satellite,,POINT(37.981040475396064 3.602856791452499),402,Non-crop
+403,403,38.14269055562707,0.6772356097837042,adadebay@umd.edu,false,2023-03-06 13:21,4.3 secs,Mapbox Satellite,,POINT(38.14269055562707 0.677235609783704),403,Non-crop
+404,404,34.46795120792515,0.6249513233906141,adadebay@umd.edu,false,2023-03-06 13:21,9.2 secs,Mapbox Satellite,,POINT(34.46795120792515 0.624951323390614),404,Crop
+405,405,35.18905016180328,-0.9303939864286976,adadebay@umd.edu,false,2023-03-06 13:21,4.5 secs,Mapbox Satellite,,POINT(35.18905016180328 -0.930393986428698),405,Crop
+406,406,34.680031744225815,-1.056354575251415,adadebay@umd.edu,false,2023-03-06 13:21,10.6 secs,Mapbox Satellite,,POINT(34.680031744225815 -1.056354575251415),406,Non-crop
+407,407,38.584107662616205,-4.000071152288873,adadebay@umd.edu,false,2023-03-06 13:21,5.5 secs,Mapbox Satellite,,POINT(38.584107662616205 -4.000071152288873),407,Non-crop
+408,408,34.54922635792607,0.568676232467724,adadebay@umd.edu,false,2023-03-06 13:22,6.8 secs,Mapbox Satellite,,POINT(34.54922635792607 0.568676232467724),408,Non-crop
+409,409,34.46976620330974,0.5891906542491239,adadebay@umd.edu,false,2023-03-06 13:22,5.0 secs,Mapbox Satellite,,POINT(34.46976620330974 0.589190654249124),409,Crop
+410,410,34.317026783547334,-0.14004161925952469,adadebay@umd.edu,false,2023-03-06 13:22,6.1 secs,Mapbox Satellite,,POINT(34.317026783547334 -0.140041619259525),410,Crop
+411,411,37.67958111982131,-0.031077482460660336,adadebay@umd.edu,false,2023-03-06 13:22,5.2 secs,Mapbox Satellite,,POINT(37.67958111982131 -0.03107748246066),411,Non-crop
+412,412,34.97698104242654,0.5048837047733515,adadebay@umd.edu,false,2023-03-06 13:23,76.0 secs,Mapbox Satellite,,POINT(34.97698104242654 0.504883704773352),412,Non-crop
+413,413,39.42808479465304,2.647284919989776,adadebay@umd.edu,false,2023-03-06 13:23,6.3 secs,Mapbox Satellite,,POINT(39.42808479465304 2.647284919989776),413,Non-crop
+414,414,36.941839061604995,3.049551867540078,adadebay@umd.edu,false,2023-03-06 13:23,5.4 secs,Mapbox Satellite,,POINT(36.941839061604995 3.049551867540078),414,Non-crop
+415,415,34.79602405819177,0.09970469632021167,adadebay@umd.edu,false,2023-03-06 13:23,6.1 secs,Mapbox Satellite,,POINT(34.79602405819177 0.099704696320212),415,Non-crop
+416,416,34.566268622694864,-0.9309187229149422,adadebay@umd.edu,false,2023-03-06 13:24,23.4 secs,Sentinel-2,,POINT(34.566268622694864 -0.930918722914942),416,Crop
+417,417,34.193493927332696,-0.7542905932579258,adadebay@umd.edu,false,2023-03-06 13:24,30.9 secs,Mapbox Satellite,,POINT(34.193493927332696 -0.754290593257926),417,Non-crop
+418,418,37.15096183503388,-0.33350560480048325,adadebay@umd.edu,false,2023-03-06 13:24,5.7 secs,Mapbox Satellite,,POINT(37.15096183503388 -0.333505604800483),418,Crop
+419,419,34.51308490339396,-0.7547247822555792,adadebay@umd.edu,false,2023-03-06 13:25,6.8 secs,Mapbox Satellite,,POINT(34.51308490339396 -0.754724782255579),419,Crop
+420,420,36.50791814531247,-0.7870475476290038,adadebay@umd.edu,false,2023-03-06 13:26,116.6 secs,Sentinel-2,,POINT(36.50791814531247 -0.787047547629004),420,Crop
+421,421,37.82868384399238,3.714236460960642,adadebay@umd.edu,false,2023-03-06 13:27,8.4 secs,Mapbox Satellite,,POINT(37.82868384399238 3.714236460960642),421,Non-crop
+422,422,40.009040766686724,-0.3419198637588498,adadebay@umd.edu,false,2023-03-06 13:27,6.2 secs,Mapbox Satellite,,POINT(40.009040766686724 -0.34191986375885),422,Non-crop
+423,423,34.491276416658955,-1.2346310142928727,adadebay@umd.edu,false,2023-03-06 13:27,5.6 secs,Mapbox Satellite,,POINT(34.491276416658955 -1.234631014292873),423,Crop
+424,424,35.15441964409439,-0.43733764965412875,adadebay@umd.edu,false,2023-03-06 13:27,5.3 secs,Mapbox Satellite,,POINT(35.15441964409439 -0.437337649654129),424,Non-crop
+425,425,35.28686492785766,4.213669588373389,adadebay@umd.edu,false,2023-03-06 13:27,7.9 secs,Mapbox Satellite,,POINT(35.28686492785766 4.213669588373389),425,Non-crop
+426,426,40.499705577963645,3.056516667636345,adadebay@umd.edu,false,2023-03-06 13:27,4.4 secs,Mapbox Satellite,,POINT(40.499705577963645 3.056516667636345),426,Non-crop
+427,427,40.30464352437439,-0.4326296899359431,adadebay@umd.edu,false,2023-03-06 13:27,8.6 secs,Mapbox Satellite,,POINT(40.30464352437439 -0.432629689935943),427,Non-crop
+428,428,39.34917441922158,1.099228413243411,adadebay@umd.edu,false,2023-03-06 13:27,13.8 secs,Mapbox Satellite,,POINT(39.34917441922158 1.099228413243411),428,Non-crop
+429,429,34.26078466118539,-1.0247476972595801,adadebay@umd.edu,false,2023-03-06 13:28,38.0 secs,Mapbox Satellite,,POINT(34.26078466118539 -1.02474769725958),429,Crop
+430,430,39.74078659873234,-1.1870999208832935,adadebay@umd.edu,false,2023-03-06 13:28,7.2 secs,Mapbox Satellite,,POINT(39.74078659873234 -1.187099920883294),430,Non-crop
+431,431,34.76303145495171,0.4669028149604851,adadebay@umd.edu,false,2023-03-06 13:28,5.2 secs,Mapbox Satellite,,POINT(34.76303145495171 0.466902814960485),431,Crop
+432,432,37.39470110843973,-0.5501557583050474,adadebay@umd.edu,false,2023-03-06 13:28,4.8 secs,Mapbox Satellite,,POINT(37.39470110843973 -0.550155758305047),432,Non-crop
+433,433,37.174734166271435,-2.02859100605471,adadebay@umd.edu,false,2023-03-06 13:28,4.7 secs,Mapbox Satellite,,POINT(37.174734166271435 -2.02859100605471),433,Non-crop
+434,434,35.7713835612932,1.7742478484709758,adadebay@umd.edu,false,2023-03-06 13:29,5.1 secs,Mapbox Satellite,,POINT(35.7713835612932 1.774247848470976),434,Non-crop
+435,435,35.054458324171065,-0.44070243296815076,adadebay@umd.edu,false,2023-03-06 13:29,7.3 secs,Mapbox Satellite,,POINT(35.054458324171065 -0.440702432968151),435,Crop
+436,436,35.535317107443134,4.0300216964176565,adadebay@umd.edu,false,2023-03-06 13:29,4.8 secs,Mapbox Satellite,,POINT(35.535317107443134 4.030021696417656),436,Non-crop
+437,437,36.195615641572736,1.2500737632246104,adadebay@umd.edu,false,2023-03-06 13:29,8.0 secs,Mapbox Satellite,,POINT(36.195615641572736 1.25007376322461),437,Non-crop
+438,438,34.585500564173465,-1.1006159436375704,adadebay@umd.edu,false,2023-03-06 13:29,5.2 secs,Mapbox Satellite,,POINT(34.585500564173465 -1.10061594363757),438,Crop
+439,439,36.93681116460859,-0.8568247999425626,adadebay@umd.edu,false,2023-03-06 13:29,10.5 secs,Mapbox Satellite,,POINT(36.93681116460859 -0.856824799942563),439,Non-crop
+440,440,34.653940619977,-1.2979980707441048,adadebay@umd.edu,false,2023-03-06 13:29,6.5 secs,Mapbox Satellite,,POINT(34.653940619977 -1.297998070744105),440,Non-crop
+441,441,39.95325288909631,-3.0539655940358257,adadebay@umd.edu,false,2023-03-06 13:29,5.1 secs,Mapbox Satellite,,POINT(39.95325288909631 -3.053965594035826),441,Non-crop
+442,442,34.79643269660636,0.954060045767657,adadebay@umd.edu,false,2023-03-06 13:30,12.3 secs,Mapbox Satellite,,POINT(34.79643269660636 0.954060045767657),442,Non-crop
+443,443,37.64560569646061,-0.4995748989339322,adadebay@umd.edu,false,2023-03-06 13:30,14.1 secs,Mapbox Satellite,,POINT(37.64560569646061 -0.499574898933932),443,Crop
+444,444,35.193697555577735,-1.0090949072315327,adadebay@umd.edu,false,2023-03-06 13:30,7.9 secs,Mapbox Satellite,,POINT(35.193697555577735 -1.009094907231533),444,Non-crop
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+446,446,34.314974037204585,-0.8050913043294229,adadebay@umd.edu,false,2023-03-06 13:30,7.1 secs,Mapbox Satellite,,POINT(34.314974037204585 -0.805091304329423),446,Non-crop
+447,447,40.31294294731426,-1.7926326213420247,adadebay@umd.edu,false,2023-03-06 13:30,4.8 secs,Mapbox Satellite,,POINT(40.31294294731426 -1.792632621342025),447,Non-crop
+448,448,36.91616303309522,-2.5727932209337974,adadebay@umd.edu,false,2023-03-06 13:30,6.1 secs,Mapbox Satellite,,POINT(36.91616303309522 -2.572793220933797),448,Non-crop
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+450,450,34.75482976822808,1.2027323213729808,adadebay@umd.edu,false,2023-03-06 13:31,5.2 secs,Mapbox Satellite,,POINT(34.75482976822808 1.202732321372981),450,Non-crop
+451,451,35.039639014761306,4.2483244000980145,adadebay@umd.edu,false,2023-03-06 13:31,5.1 secs,Mapbox Satellite,,POINT(35.039639014761306 4.248324400098014),451,Non-crop
+452,452,40.2874788432159,2.504938312987063,adadebay@umd.edu,false,2023-03-06 13:31,4.8 secs,Mapbox Satellite,,POINT(40.2874788432159 2.504938312987063),452,Non-crop
+453,453,37.573692021019895,-0.004673769817114266,adadebay@umd.edu,false,2023-03-06 13:31,5.9 secs,Mapbox Satellite,,POINT(37.573692021019895 -0.004673769817114),453,Crop
+454,454,36.807037684141676,2.542819879774906,adadebay@umd.edu,false,2023-03-06 13:31,5.3 secs,Mapbox Satellite,,POINT(36.807037684141676 2.542819879774906),454,Non-crop
+455,455,37.0048015872321,-0.45869277751116916,adadebay@umd.edu,false,2023-03-06 13:31,8.2 secs,Mapbox Satellite,,POINT(37.0048015872321 -0.458692777511169),455,Non-crop
+456,456,36.84196187497421,3.7249805816569435,adadebay@umd.edu,false,2023-03-06 13:31,4.8 secs,Mapbox Satellite,,POINT(36.84196187497421 3.724980581656944),456,Non-crop
+457,457,34.649646702806294,0.6912557299441862,adadebay@umd.edu,false,2023-03-06 13:31,8.1 secs,Mapbox Satellite,,POINT(34.649646702806294 0.691255729944186),457,Crop
+458,458,39.27351047995854,2.382870821022328,adadebay@umd.edu,false,2023-03-06 13:31,4.9 secs,Mapbox Satellite,,POINT(39.27351047995854 2.382870821022328),458,Non-crop
+459,459,34.09255046019023,0.13750458019816195,adadebay@umd.edu,false,2023-03-06 13:32,6.0 secs,Mapbox Satellite,,POINT(34.09255046019023 0.137504580198162),459,Non-crop
+460,460,35.196367961673985,-0.9384964043103956,adadebay@umd.edu,false,2023-03-06 13:32,5.4 secs,Mapbox Satellite,,POINT(35.196367961673985 -0.938496404310396),460,Non-crop
+461,461,34.729242476157864,-0.006280115060495967,adadebay@umd.edu,false,2023-03-06 13:32,15.0 secs,Mapbox Satellite,,POINT(34.729242476157864 -0.006280115060496),461,Non-crop
+462,462,37.14320247144663,-0.5799924116994661,adadebay@umd.edu,false,2023-03-06 13:32,5.6 secs,Mapbox Satellite,,POINT(37.14320247144663 -0.579992411699466),462,Non-crop
+463,463,34.839386313724425,-0.5907161983375747,adadebay@umd.edu,false,2023-03-06 13:32,6.4 secs,Mapbox Satellite,,POINT(34.839386313724425 -0.590716198337575),463,Crop
+464,464,37.298134606247274,-2.7530158736682773,adadebay@umd.edu,false,2023-03-06 13:32,5.6 secs,Mapbox Satellite,,POINT(37.298134606247274 -2.753015873668277),464,Non-crop
+465,465,37.235080182010115,3.2939567840656823,adadebay@umd.edu,false,2023-03-06 13:32,6.4 secs,Mapbox Satellite,,POINT(37.235080182010115 3.293956784065682),465,Non-crop
+466,466,34.620563644213945,3.1399987547652173,adadebay@umd.edu,false,2023-03-06 13:32,5.0 secs,Mapbox Satellite,,POINT(34.620563644213945 3.139998754765217),466,Non-crop
+467,467,37.90330112445743,2.0453183232955094,adadebay@umd.edu,false,2023-03-06 13:32,5.0 secs,Mapbox Satellite,,POINT(37.90330112445743 2.045318323295509),467,Non-crop
+468,468,38.75219866912606,1.4087696247600232,adadebay@umd.edu,false,2023-03-06 13:33,5.0 secs,Mapbox Satellite,,POINT(38.75219866912606 1.408769624760023),468,Non-crop
+469,469,36.09478312311987,-0.45270767372427245,adadebay@umd.edu,false,2023-03-06 13:33,10.5 secs,Mapbox Satellite,,POINT(36.09478312311987 -0.452707673724272),469,Non-crop
+470,470,39.14544637042974,3.0526918285416156,adadebay@umd.edu,false,2023-03-06 13:33,5.7 secs,Mapbox Satellite,,POINT(39.14544637042974 3.052691828541616),470,Non-crop
+471,471,36.986050045933645,0.8166083567094135,adadebay@umd.edu,false,2023-03-06 13:33,26.4 secs,Mapbox Satellite,,POINT(36.986050045933645 0.816608356709414),471,Non-crop
+472,472,36.67508229636412,2.360931840234958,adadebay@umd.edu,false,2023-03-06 13:33,4.1 secs,Mapbox Satellite,,POINT(36.67508229636412 2.360931840234958),472,Non-crop
+473,473,34.87929322199816,-1.3410826721831222,adadebay@umd.edu,false,2023-03-06 13:33,6.7 secs,Mapbox Satellite,,POINT(34.87929322199816 -1.341082672183122),473,Non-crop
+474,474,36.96034269804192,0.6752585022986302,adadebay@umd.edu,false,2023-03-06 13:34,5.8 secs,Mapbox Satellite,,POINT(36.96034269804192 0.67525850229863),474,Non-crop
+475,475,38.01094738788046,0.4034110699662577,adadebay@umd.edu,false,2023-03-06 13:34,5.2 secs,Mapbox Satellite,,POINT(38.01094738788046 0.403411069966258),475,Non-crop
+476,476,37.678195029831784,0.4669841625774332,adadebay@umd.edu,false,2023-03-06 13:34,5.2 secs,Mapbox Satellite,,POINT(37.678195029831784 0.466984162577433),476,Non-crop
+477,477,35.56489986263098,4.17275389765291,adadebay@umd.edu,false,2023-03-06 13:34,4.2 secs,Mapbox Satellite,,POINT(35.56489986263098 4.17275389765291),477,Non-crop
+478,478,38.89991542855711,-0.10599067675841689,adadebay@umd.edu,false,2023-03-06 13:34,4.8 secs,Mapbox Satellite,,POINT(38.89991542855711 -0.105990676758417),478,Non-crop
+479,479,38.16088744929678,-0.5340005076082127,adadebay@umd.edu,false,2023-03-06 13:34,26.5 secs,Sentinel-2,,POINT(38.16088744929678 -0.534000507608213),479,Non-crop
+480,480,36.31334231145933,1.7564825870660954,adadebay@umd.edu,false,2023-03-06 13:35,10.2 secs,Mapbox Satellite,,POINT(36.31334231145933 1.756482587066095),480,Non-crop
+481,481,36.05446021526722,-0.7701864509339067,adadebay@umd.edu,false,2023-03-06 13:35,18.1 secs,Mapbox Satellite,,POINT(36.05446021526722 -0.770186450933907),481,Non-crop
+482,482,34.634601312187485,0.08943773979142051,adadebay@umd.edu,false,2023-03-06 13:35,6.4 secs,Mapbox Satellite,,POINT(34.634601312187485 0.089437739791421),482,Crop
+483,483,37.91044452150111,3.5311304324163824,adadebay@umd.edu,false,2023-03-06 13:35,5.0 secs,Mapbox Satellite,,POINT(37.91044452150111 3.531130432416382),483,Non-crop
+484,484,34.5575298635729,-1.2831810660561516,adadebay@umd.edu,false,2023-03-06 13:35,12.7 secs,Mapbox Satellite,,POINT(34.5575298635729 -1.283181066056152),484,Crop
+485,485,35.39516029245672,-0.8794258046257776,adadebay@umd.edu,false,2023-03-06 13:36,22.2 secs,Mapbox Satellite,,POINT(35.39516029245672 -0.879425804625778),485,Non-crop
+486,486,38.321928188337154,3.043383821526915,adadebay@umd.edu,false,2023-03-06 13:36,5.4 secs,Mapbox Satellite,,POINT(38.321928188337154 3.043383821526915),486,Non-crop
+487,487,39.34805366474005,0.5760758805466408,adadebay@umd.edu,false,2023-03-06 13:36,4.7 secs,Mapbox Satellite,,POINT(39.34805366474005 0.576075880546641),487,Non-crop
+488,488,40.70228392102145,2.2930049702457507,adadebay@umd.edu,false,2023-03-06 13:36,4.1 secs,Mapbox Satellite,,POINT(40.70228392102145 2.293004970245751),488,Non-crop
+489,489,34.988652426404,2.685077579713715,adadebay@umd.edu,false,2023-03-06 13:36,5.0 secs,Mapbox Satellite,,POINT(34.988652426404 2.685077579713715),489,Non-crop
+490,490,35.0036305876314,-0.7040441877978902,adadebay@umd.edu,false,2023-03-06 13:36,9.7 secs,Mapbox Satellite,,POINT(35.0036305876314 -0.70404418779789),490,Non-crop
+491,491,34.653443139440675,-0.02515374865425375,adadebay@umd.edu,false,2023-03-06 13:36,5.4 secs,Mapbox Satellite,,POINT(34.653443139440675 -0.025153748654254),491,Non-crop
+492,492,40.614414543790275,0.3435724730712614,adadebay@umd.edu,false,2023-03-06 13:36,6.3 secs,Mapbox Satellite,,POINT(40.614414543790275 0.343572473071261),492,Non-crop
+493,493,38.51702418202515,-2.8238115957339627,adadebay@umd.edu,false,2023-03-06 13:36,4.8 secs,Mapbox Satellite,,POINT(38.51702418202515 -2.823811595733963),493,Non-crop
+494,494,34.383934200549206,4.452384575378512,adadebay@umd.edu,false,2023-03-06 13:36,4.5 secs,Mapbox Satellite,,POINT(34.383934200549206 4.452384575378512),494,Non-crop
+495,495,37.70530305115963,2.5868444101204076,adadebay@umd.edu,false,2023-03-06 13:37,3.7 secs,Mapbox Satellite,,POINT(37.70530305115963 2.586844410120408),495,Non-crop
+496,496,35.33655489953117,-1.367002453719124,adadebay@umd.edu,false,2023-03-06 13:37,4.0 secs,Mapbox Satellite,,POINT(35.33655489953117 -1.367002453719124),496,Non-crop
+497,497,38.39506049495438,2.5714102466385724,adadebay@umd.edu,false,2023-03-06 13:37,4.7 secs,Mapbox Satellite,,POINT(38.39506049495438 2.571410246638572),497,Non-crop
+498,498,39.084849466631404,3.2272009282858454,adadebay@umd.edu,false,2023-03-06 13:37,5.1 secs,Mapbox Satellite,,POINT(39.084849466631404 3.227200928285845),498,Non-crop
+499,499,37.09093189496214,0.9915204603295135,adadebay@umd.edu,false,2023-03-06 13:37,4.1 secs,Mapbox Satellite,,POINT(37.09093189496214 0.991520460329514),499,Non-crop
+500,500,37.349874791030274,-2.6561425061602493,adadebay@umd.edu,false,2023-03-06 13:37,5.0 secs,Mapbox Satellite,,POINT(37.349874791030274 -2.656142506160249),500,Non-crop
+501,501,35.17501834522299,-0.7515855198732806,adadebay@umd.edu,false,2023-03-06 13:38,74.6 secs,Mapbox Satellite,,POINT(35.17501834522299 -0.751585519873281),501,Non-crop
+502,502,35.71291563450446,-0.6426003621868701,adadebay@umd.edu,false,2023-03-06 13:38,5.3 secs,Mapbox Satellite,,POINT(35.71291563450446 -0.64260036218687),502,Non-crop
+503,503,34.73484872536813,-0.8482526044699542,adadebay@umd.edu,false,2023-03-06 13:38,6.2 secs,Mapbox Satellite,,POINT(34.73484872536813 -0.848252604469954),503,Crop
+504,504,39.023752295479596,-1.6523111301971936,adadebay@umd.edu,false,2023-03-06 13:38,4.8 secs,Mapbox Satellite,,POINT(39.023752295479596 -1.652311130197194),504,Non-crop
+505,505,40.78764234677859,2.693632786095179,adadebay@umd.edu,false,2023-03-06 13:38,4.4 secs,Mapbox Satellite,,POINT(40.78764234677859 2.693632786095179),505,Non-crop
+506,506,35.06000147333601,-0.7966242423341563,adadebay@umd.edu,false,2023-03-06 13:39,5.3 secs,Mapbox Satellite,,POINT(35.06000147333601 -0.796624242334156),506,Non-crop
+507,507,36.30009993341707,-0.8232024117545933,adadebay@umd.edu,false,2023-03-06 13:39,5.0 secs,Mapbox Satellite,,POINT(36.30009993341707 -0.823202411754593),507,Non-crop
+508,508,35.10989219436885,-0.32787050503899345,adadebay@umd.edu,false,2023-03-06 13:39,21.2 secs,Mapbox Satellite,,POINT(35.10989219436885 -0.327870505038993),508,Crop
+509,509,34.828144212308935,-0.5773129039263161,adadebay@umd.edu,false,2023-03-06 13:40,72.7 secs,Mapbox Satellite,,POINT(34.828144212308935 -0.577312903926316),509,Non-crop
+510,510,35.97078220549668,2.9560655204795507,adadebay@umd.edu,false,2023-03-06 13:40,6.6 secs,Mapbox Satellite,,POINT(35.97078220549668 2.956065520479551),510,Non-crop
+511,511,35.10284238847304,-0.4966636896018876,adadebay@umd.edu,false,2023-03-06 13:40,6.3 secs,Mapbox Satellite,,POINT(35.10284238847304 -0.496663689601888),511,Non-crop
+512,512,40.132913213558695,-3.146251638192202,adadebay@umd.edu,false,2023-03-06 13:41,5.7 secs,Mapbox Satellite,,POINT(40.132913213558695 -3.146251638192202),512,Non-crop
+513,513,34.22388893299226,0.605241533460933,adadebay@umd.edu,false,2023-03-06 13:41,6.7 secs,Mapbox Satellite,,POINT(34.22388893299226 0.605241533460933),513,Crop
+514,514,38.96670779650444,-0.4182302700090625,adadebay@umd.edu,false,2023-03-06 13:41,4.4 secs,Mapbox Satellite,,POINT(38.96670779650444 -0.418230270009062),514,Non-crop
+515,515,40.32965437189055,1.3051310381454755,adadebay@umd.edu,false,2023-03-06 13:41,4.0 secs,Mapbox Satellite,,POINT(40.32965437189055 1.305131038145476),515,Non-crop
+516,516,34.96478463720174,-0.5794803589111308,adadebay@umd.edu,false,2023-03-06 13:41,8.0 secs,Mapbox Satellite,,POINT(34.96478463720174 -0.579480358911131),516,Crop
+517,517,40.96226183600945,-0.2882607556531856,adadebay@umd.edu,false,2023-03-06 13:41,5.5 secs,Mapbox Satellite,,POINT(40.96226183600945 -0.288260755653186),517,Non-crop
+518,518,39.735173373793074,3.116710754764235,adadebay@umd.edu,false,2023-03-06 13:41,6.6 secs,Mapbox Satellite,,POINT(39.735173373793074 3.116710754764235),518,Non-crop
+519,519,36.82035819397363,1.9556658604114368,adadebay@umd.edu,false,2023-03-06 13:41,4.2 secs,Mapbox Satellite,,POINT(36.82035819397363 1.955665860411437),519,Non-crop
+520,520,35.35383455962234,2.211003394302324,adadebay@umd.edu,false,2023-03-06 13:41,3.8 secs,Mapbox Satellite,,POINT(35.35383455962234 2.211003394302324),520,Non-crop
+521,521,35.410735825610544,-1.428738022679518,adadebay@umd.edu,false,2023-03-06 13:41,4.9 secs,Mapbox Satellite,,POINT(35.410735825610544 -1.428738022679518),521,Non-crop
+522,522,37.19939040219627,2.0807162473361056,adadebay@umd.edu,false,2023-03-06 13:41,5.9 secs,Mapbox Satellite,,POINT(37.19939040219627 2.080716247336106),522,Non-crop
+523,523,35.08208831520865,-0.9479913632950039,adadebay@umd.edu,false,2023-03-06 13:43,78.6 secs,Mapbox Satellite,,POINT(35.08208831520865 -0.947991363295004),523,Crop
+524,524,36.529750333470275,-1.2510182266385572,adadebay@umd.edu,false,2023-03-06 13:43,4.9 secs,Mapbox Satellite,,POINT(36.529750333470275 -1.251018226638557),524,Non-crop
+525,525,34.93545090273626,1.3595428403458083,adadebay@umd.edu,false,2023-03-06 13:43,5.2 secs,Mapbox Satellite,,POINT(34.93545090273626 1.359542840345808),525,Non-crop
+526,526,34.71477847358641,0.00774202647017627,adadebay@umd.edu,false,2023-03-06 13:44,56.7 secs,Sentinel-2,,POINT(34.71477847358641 0.007742026470176),526,Crop
+527,527,39.59186188819468,-1.1566710064881998,adadebay@umd.edu,false,2023-03-06 13:44,5.9 secs,Mapbox Satellite,,POINT(39.59186188819468 -1.1566710064882),527,Non-crop
+528,528,38.23861965981843,0.33277349267400996,adadebay@umd.edu,false,2023-03-06 13:44,5.1 secs,Mapbox Satellite,,POINT(38.23861965981843 0.33277349267401),528,Non-crop
+529,529,37.01526073538915,-0.7368748435057784,adadebay@umd.edu,false,2023-03-06 13:44,5.9 secs,Mapbox Satellite,,POINT(37.01526073538915 -0.736874843505778),529,Non-crop
+530,530,38.09145658119295,-2.415198363979528,adadebay@umd.edu,false,2023-03-06 13:45,32.7 secs,Planet Monthly Mosaics,,POINT(38.09145658119295 -2.415198363979528),530,Crop
+531,531,37.65093779234663,-0.026120886979007708,adadebay@umd.edu,false,2023-03-06 13:45,11.2 secs,Mapbox Satellite,,POINT(37.65093779234663 -0.026120886979008),531,Non-crop
+532,532,40.536095203749554,3.632313062717191,adadebay@umd.edu,false,2023-03-06 13:45,12.5 secs,Mapbox Satellite,,POINT(40.536095203749554 3.632313062717191),532,Non-crop
+533,533,34.56842069799163,0.8483574983470237,adadebay@umd.edu,false,2023-03-06 13:46,13.4 secs,Mapbox Satellite,,POINT(34.56842069799163 0.848357498347024),533,Crop
+534,534,34.560156122324,-0.01247945700671197,adadebay@umd.edu,false,2023-03-06 13:46,16.9 secs,Mapbox Satellite,,POINT(34.560156122324 -0.012479457006712),534,Crop
+535,535,36.52662305326271,1.9856940678472765,adadebay@umd.edu,false,2023-03-06 13:46,4.9 secs,Mapbox Satellite,,POINT(36.52662305326271 1.985694067847276),535,Non-crop
+536,536,36.814465210482616,-1.0359101389648762,adadebay@umd.edu,false,2023-03-06 13:46,13.4 secs,Mapbox Satellite,,POINT(36.814465210482616 -1.035910138964876),536,Crop
+537,537,37.06018956428111,-0.4699689113578657,adadebay@umd.edu,false,2023-03-06 13:46,5.9 secs,Mapbox Satellite,,POINT(37.06018956428111 -0.469968911357866),537,Non-crop
+538,538,39.14612330417667,2.7377571101253593,adadebay@umd.edu,false,2023-03-06 13:46,6.0 secs,Mapbox Satellite,,POINT(39.14612330417667 2.737757110125359),538,Non-crop
+539,539,37.87082220460906,-1.7089443100333617,adadebay@umd.edu,false,2023-03-06 13:47,18.9 secs,Mapbox Satellite,,POINT(37.87082220460906 -1.708944310033362),539,Non-crop
+540,540,34.88255928203005,-0.023363158733180508,adadebay@umd.edu,false,2023-03-06 13:47,5.9 secs,Mapbox Satellite,,POINT(34.88255928203005 -0.023363158733181),540,Non-crop
+541,541,34.46410083114127,-0.8173018845840876,adadebay@umd.edu,false,2023-03-06 13:49,151.5 secs,Mapbox Satellite,,POINT(34.46410083114127 -0.817301884584088),541,Non-crop
+542,542,34.93492776673225,2.8648969176507055,adadebay@umd.edu,false,2023-03-06 13:49,7.8 secs,Mapbox Satellite,,POINT(34.93492776673225 2.864896917650706),542,Non-crop
+543,543,38.81338965106459,1.119704331352858,adadebay@umd.edu,false,2023-03-06 13:50,5.7 secs,Mapbox Satellite,,POINT(38.81338965106459 1.119704331352858),543,Non-crop
diff --git a/maps/Kenya_2019/Kenya_area_estimate.ipynb b/maps/Kenya_2019/Kenya_area_estimate.ipynb
new file mode 100644
index 00000000..7a2a98c6
--- /dev/null
+++ b/maps/Kenya_2019/Kenya_area_estimate.ipynb
@@ -0,0 +1,2213 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "778ff440"
+      },
+      "source": [
+        "# Intercomparison\n",
+        "\n",
+        "**Author:** Adebowale Adebayo\n",
+        "\n",
+        "**Last updated:** March 23, 2024\n",
+        "\n",
+        "**Description:** Runs intercomparison and **area estimate** for Kenya 2019\n",
+        "\n",
+        "## 1. Setup"
+      ],
+      "id": "778ff440"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fb42d13c"
+      },
+      "outputs": [],
+      "source": [
+        "# !earthengine authenticate"
+      ],
+      "id": "fb42d13c"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hZ8qzSlB75kl"
+      },
+      "outputs": [],
+      "source": [
+        "!git clone https://github.com/nasaharvest/crop-mask.git"
+      ],
+      "id": "hZ8qzSlB75kl"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "1fe-6D3f8LTb",
+        "outputId": "66566b67-ed93-4144-a4a1-25142c893d4e"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/crop-mask\n"
+          ]
+        }
+      ],
+      "source": [
+        "%cd crop-mask/"
+      ],
+      "id": "1fe-6D3f8LTb"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gEUyxHk9MEU2"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install cartopy -qq\n",
+        "!pip install rasterio -qq\n",
+        "!pip install dvc[gs] -qq"
+      ],
+      "id": "gEUyxHk9MEU2"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!git checkout area-estimate-from-multi-land-cover"
+      ],
+      "metadata": {
+        "id": "V-RT5I2kXJt4"
+      },
+      "id": "V-RT5I2kXJt4",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "9907f9a5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "outputId": "a5344002-4c63-4f07-860c-179f995d958c"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_1_states_provinces.zip\n",
+            "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n"
+          ]
+        }
+      ],
+      "source": [
+        "import ee\n",
+        "import geemap\n",
+        "import sys\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "import geopandas as gpd\n",
+        "from pathlib import Path\n",
+        "\n",
+        "ee.Authenticate()\n",
+        "ee.Initialize(project=\"bsos-geog-harvest1\")\n",
+        "\n",
+        "sys.path.append(\"../..\")\n",
+        "\n",
+        "from src.compare_covermaps import TARGETS, filter_by_bounds, generate_report, CLASS_COL, COUNTRY_COL, get_ensemble_area\n",
+        "from src.compare_covermaps import TEST_COUNTRIES, TEST_CODE"
+      ],
+      "id": "9907f9a5"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c61ea4f8"
+      },
+      "source": [
+        "## 2. Read in evaluation set"
+      ],
+      "id": "c61ea4f8"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "7f75e567",
+        "outputId": "4eeacc2e-61f1-41a8-bbef-f082001aa56d"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "country = \"Kenya\"\n",
+        "\n",
+        "if country not in TEST_CODE:\n",
+        "    print(f\"WARNING: {country} not found in TEST_CODE in src/compare_covermaps.py\")\n",
+        "if country not in TEST_COUNTRIES:\n",
+        "    print(f\"WARNING: {country} not found in TEST_COUNTRIES in src/compare_covermaps.py\")\n",
+        "if country not in TEST_CODE or country not in TEST_COUNTRIES:\n",
+        "    print(\"Please update src/compare_covermaps.py and restart the notebook.\")\n",
+        "else:\n",
+        "    country_code = TEST_CODE[country]"
+      ],
+      "id": "7f75e567"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ceo_set1 = './data/ref_samples_area/ceo-Kenya-Crop-Area-Estimation-Reference-Sample-2019---Set-1-sample-data-2024-03-14.csv'\n",
+        "ceo_set2 = './data/ref_samples_area/ceo-Kenya-Crop-Area-Estimation-Reference-Sample-2019---Set-2-sample-data-2024-03-14.csv'"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "66-YJBNxYAdF",
+        "outputId": "ad46f2f6-7de6-402c-99e1-9bde45648b34"
+      },
+      "id": "66-YJBNxYAdF",
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def reference_sample_agree(ceo_ref1, ceo_ref2):\n",
+        "    ceo_ref1 = pd.read_csv(ceo_ref1)\n",
+        "    ceo_ref2 = pd.read_csv(ceo_ref2)\n",
+        "\n",
+        "    assert ceo_ref1.columns[-1] == ceo_ref2.columns[-1]\n",
+        "\n",
+        "    label_question = ceo_ref1.columns[-1]\n",
+        "\n",
+        "    print(f\"Number of NANs/ missing answers in set 1: {ceo_ref1[label_question].isna().sum()}\")\n",
+        "    print(f\"Number of NANs/ missing answers in set 2: {ceo_ref2[label_question].isna().sum()}\")\n",
+        "\n",
+        "    if ceo_ref1.shape[0] != ceo_ref2.shape[0]:\n",
+        "        print(\"The number of rows in the reference sets are not equal.\")\n",
+        "        print(\"Checking for duplictes on 'plotid'..\")\n",
+        "        print(\n",
+        "            \" Number of duplicated in set 1: %s\" % ceo_ref1[ceo_ref1.plotid.duplicated()].shape[0]\n",
+        "        )\n",
+        "        print(\n",
+        "            \" Number of duplicated in set 2: %s\" % ceo_ref2[ceo_ref2.plotid.duplicated()].shape[0]\n",
+        "        )\n",
+        "        print(\"Removing duplicates and keeping the first...\")\n",
+        "        ceo_ref1 = ceo_ref1.drop_duplicates(subset=\"plotid\", keep=\"first\")\n",
+        "        ceo_ref2 = ceo_ref2.drop_duplicates(subset=\"plotid\", keep=\"first\")\n",
+        "\n",
+        "        ceo_ref1.set_index(\"plotid\", inplace=True)\n",
+        "        ceo_ref2.set_index(\"plotid\", inplace=True)\n",
+        "    else:\n",
+        "        print(\"The number of rows in the reference sets are equal.\")\n",
+        "\n",
+        "    ceo_agree = ceo_ref1[ceo_ref1[label_question] == ceo_ref2[label_question]]\n",
+        "\n",
+        "    print(\n",
+        "        \"Number of samples that are in agreement: %d out of %d (%.2f%%)\"\n",
+        "        % (\n",
+        "            ceo_agree.shape[0],\n",
+        "            ceo_ref1.shape[0],\n",
+        "            ceo_agree.shape[0] / ceo_ref1.shape[0] * 100,\n",
+        "        )\n",
+        "    )\n",
+        "    ceo_agree_geom = gpd.GeoDataFrame(\n",
+        "        ceo_agree,\n",
+        "        geometry=gpd.points_from_xy(ceo_agree.lon, ceo_agree.lat),\n",
+        "        crs=\"EPSG:4326\",\n",
+        "    )\n",
+        "\n",
+        "    label_responses = ceo_agree_geom[label_question].unique()\n",
+        "    assert len(label_responses) == 2\n",
+        "\n",
+        "    for r, row in ceo_agree_geom.iterrows():\n",
+        "\n",
+        "        try:\n",
+        "            if (\n",
+        "                row[label_question].lower() == \"crop\"\n",
+        "                or row[label_question].lower() == \"cropland\"\n",
+        "                or row[label_question].lower() == \"planted\"\n",
+        "            ):\n",
+        "                ceo_agree_geom.loc[r, CLASS_COL] = 1\n",
+        "            elif(\n",
+        "                row[label_question].lower() == \"non-crop\"\n",
+        "                or row[label_question].lower() == \"non-cropland\"\n",
+        "                or row[label_question].lower() == \"not planted\"\n",
+        "            ):\n",
+        "                ceo_agree_geom.loc[r, CLASS_COL] = 0\n",
+        "        except IndexError:\n",
+        "            ceo_agree_geom.loc[r, CLASS_COL] = 255\n",
+        "\n",
+        "    ceo_agree_geom = ceo_agree_geom[ceo_agree_geom[CLASS_COL] != 255]\n",
+        "\n",
+        "    ceo_agree_geom[CLASS_COL] = ceo_agree_geom[CLASS_COL].astype(int)\n",
+        "    ceo_agree_geom[COUNTRY_COL] = country\n",
+        "    ceo_agree_geom = ceo_agree_geom[['lat','lon',CLASS_COL, COUNTRY_COL, 'geometry']]\n",
+        "\n",
+        "    return ceo_agree_geom"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "24QIyHfcZOeG",
+        "outputId": "94a3b69d-9f02-4dd3-df91-cc6618ec3cfa"
+      },
+      "id": "24QIyHfcZOeG",
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gdf = reference_sample_agree(ceo_set1,ceo_set2)\n",
+        "gdf = filter_by_bounds(country_code=country_code, gdf=gdf)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 88
+        },
+        "id": "QXMdHSHVauqV",
+        "outputId": "bf082047-7270-4f61-c914-61ab732171ab"
+      },
+      "id": "QXMdHSHVauqV",
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of NANs/ missing answers in set 1: 2\n",
+            "Number of NANs/ missing answers in set 2: 0\n",
+            "The number of rows in the reference sets are equal.\n",
+            "Number of samples that are in agreement: 487 out of 544 (89.52%)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "31341d98"
+      },
+      "source": [
+        "## 3. Run intercomparison and area estimation"
+      ],
+      "id": "31341d98"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "ImkKe6cEB4aB",
+        "outputId": "c3865269-5698-41bb-f3a7-8ee6927df9a0"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        lat        lon  binary country                   geometry\n",
+              "0  2.403553  34.988630       0   Kenya   POINT (34.98863 2.40355)\n",
+              "1  0.237452  40.436913       0   Kenya   POINT (40.43691 0.23745)\n",
+              "2  4.203261  35.525785       0   Kenya   POINT (35.52579 4.20326)\n",
+              "3  1.786133  35.345029       0   Kenya   POINT (35.34503 1.78613)\n",
+              "4 -2.088707  41.092916       1   Kenya  POINT (41.09292 -2.08871)"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9f825a85-3147-4c88-9edf-376d2124bde0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>lat</th>\n",
+              "      <th>lon</th>\n",
+              "      <th>binary</th>\n",
+              "      <th>country</th>\n",
+              "      <th>geometry</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2.403553</td>\n",
+              "      <td>34.988630</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Kenya</td>\n",
+              "      <td>POINT (34.98863 2.40355)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.237452</td>\n",
+              "      <td>40.436913</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Kenya</td>\n",
+              "      <td>POINT (40.43691 0.23745)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>4.203261</td>\n",
+              "      <td>35.525785</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Kenya</td>\n",
+              "      <td>POINT (35.52579 4.20326)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.786133</td>\n",
+              "      <td>35.345029</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Kenya</td>\n",
+              "      <td>POINT (35.34503 1.78613)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>-2.088707</td>\n",
+              "      <td>41.092916</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Kenya</td>\n",
+              "      <td>POINT (41.09292 -2.08871)</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f825a85-3147-4c88-9edf-376d2124bde0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9f825a85-3147-4c88-9edf-376d2124bde0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9f825a85-3147-4c88-9edf-376d2124bde0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-4143330b-b171-4e85-9a38-24325a9901e8\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4143330b-b171-4e85-9a38-24325a9901e8')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-4143330b-b171-4e85-9a38-24325a9901e8 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "gdf",
+              "summary": "{\n  \"name\": \"gdf\",\n  \"rows\": 487,\n  \"fields\": [\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9148026720101181,\n        \"min\": -4.3996825162698165,\n        \"max\": 4.509081535107014,\n        \"num_unique_values\": 487,\n        \"samples\": [\n          -0.3086155983480622,\n          0.2319312752100853,\n          -0.7040441877978902\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9654035941659458,\n        \"min\": 33.98152361411099,\n        \"max\": 41.80068122597383,\n        \"num_unique_values\": 487,\n        \"samples\": [\n          35.89383258961963,\n          37.34840090595225,\n          35.0036305876314\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Kenya\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geometry\",\n      \"properties\": {\n        \"dtype\": \"geometry\",\n        \"num_unique_values\": 487,\n        \"samples\": [\n          \"POINT (35.89383258961963 -0.3086155983480622)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ],
+      "source": [
+        "gdf.head()"
+      ],
+      "id": "ImkKe6cEB4aB"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "54c4cc0f",
+        "outputId": "301462fd-139e-4c90-f4d8-1ff074dc5bc8"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "TARGETS = {k:v for k,v in TARGETS.items()}\n",
+        "for k, v in TARGETS.items():\n",
+        "    if country not in v.countries:\n",
+        "        continue\n",
+        "    if v.year is None:\n",
+        "        v.year = v.collection_years[v.countries.index(country)]"
+      ],
+      "id": "54c4cc0f"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "1oQjubrHjkBi",
+        "outputId": "7faeaf18-9f29-4c84-a608-c54b83c4b908"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "reference_year = 2019\n",
+        "# for i in range(reference_year -1, reference_year+2):\n",
+        "    # print(i)\n",
+        "TARGETS = {k: v for k, v in TARGETS.items() if v.year in range(reference_year - 1, reference_year + 2)} #[reference_year - 1, reference_year, reference_year + 1]}"
+      ],
+      "id": "1oQjubrHjkBi"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "id": "98e241d2",
+        "outputId": "e14e0bcb-1a28-4d0e-f6a9-cf97e869531c"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[Kenya] sampling copernicus...\n",
+            "[Kenya] sampling worldcover-v100...\n",
+            "[Kenya] sampling glad...\n",
+            "[Kenya] sampling dynamicworld...\n",
+            "[Kenya] sampling digital-earth-africa...\n",
+            "[Kenya] sampling esri-lulc...\n",
+            "[Kenya] sampling harvest-crop-maps...\n"
+          ]
+        }
+      ],
+      "source": [
+        "for cropmap in TARGETS.values():\n",
+        "    if country not in cropmap.countries:\n",
+        "        continue\n",
+        "    print(f\"[{country}] sampling \" + cropmap.title + \"...\")\n",
+        "    map_sampled = cropmap.extract_test(gdf).copy()\n",
+        "    gdf = pd.merge(gdf, map_sampled, on=[\"lat\", \"lon\"], how=\"left\")\n",
+        "    gdf.drop_duplicates(inplace=True)  # TODO find why points get duplicated"
+      ],
+      "id": "98e241d2"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "95a0f536",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "c3fd777f-601a-48b3-d4d9-82ae315be913"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[Kenya] calculating pixel area for copernicus...\n",
+            "[Kenya] calculating pixel area for worldcover-v100...\n",
+            "[Kenya] calculating pixel area for glad...\n",
+            "[Kenya] calculating pixel area for dynamicworld...\n",
+            "[Kenya] calculating pixel area for digital-earth-africa...\n",
+            "[Kenya] calculating pixel area for esri-lulc...\n",
+            "[Kenya] calculating pixel area for harvest-crop-maps...\n"
+          ]
+        }
+      ],
+      "source": [
+        "a_j = {}\n",
+        "for cropmap in TARGETS.values():\n",
+        "    if country not in cropmap.countries:\n",
+        "        continue\n",
+        "    print(f\"[{country}] calculating pixel area for \" + cropmap.title + \"...\")\n",
+        "    a_j[cropmap.title] = cropmap.compute_map_area(country, export=True, dataset_name=cropmap.title).copy()"
+      ],
+      "id": "95a0f536"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# update a_j values with exported values\n",
+        "for cropmap in a_j.keys():\n",
+        "    try:\n",
+        "        area_df = pd.read_csv(f'./Crop_NonCrop_Area_Sum_Export-{country}-{cropmap}.csv')\n",
+        "    except:\n",
+        "        continue\n",
+        "    crop_area = int(area_df['crop_sum'][0])\n",
+        "    noncrop_area = int(area_df['noncrop_sum'][0])\n",
+        "    a_j[cropmap] = np.array([noncrop_area, crop_area])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "5fJPzvOeUo9G",
+        "outputId": "89c27437-3525-4327-c436-c381c400b5c2"
+      },
+      "id": "5fJPzvOeUo9G",
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Change None to nan\n",
+        "a_j = {k: np.array([np.nan, np.nan]) if np.any(v == None) else v for k,v in a_j.items()}"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "zyR4qCJ49Rh5",
+        "outputId": "2b9fd61b-6963-4ada-eae8-f107d619b013"
+      },
+      "id": "zyR4qCJ49Rh5",
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "LY6Q_QtUgME_",
+        "outputId": "49c1b572-af57-41aa-a39b-18fbcb56ead6"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from src.area_utils import compute_area_estimate, compute_area_error_matrix, compute_std_p_i\n",
+        "from sklearn.metrics import confusion_matrix"
+      ],
+      "id": "LY6Q_QtUgME_"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "oojPqwSboiWU",
+        "outputId": "71bab38b-1247-4086-ed50-86c4d9ddcd00"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def compute_area_estimate(dataset, true, pred, a_j, resolution, year):\n",
+        "    cm = confusion_matrix(true, pred)\n",
+        "    total_px = a_j.sum()\n",
+        "    w_j = a_j / total_px\n",
+        "\n",
+        "    am = compute_area_error_matrix(cm, w_j)\n",
+        "    a_i = am.sum(axis=1)\n",
+        "    std_a_i = compute_std_p_i(w_j, am, cm)\n",
+        "    err_a_i = 1.96 * std_a_i\n",
+        "\n",
+        "    a_px = total_px * a_i\n",
+        "    err_px = err_a_i * total_px\n",
+        "    return pd.DataFrame(\n",
+        "        data={\n",
+        "            \"dataset\": dataset,\n",
+        "            \"area_ha\": a_px[1] * (resolution**2) / (100**2),\n",
+        "            \"err_ha\": err_px[1] * (resolution**2) / (100**2),\n",
+        "            \"year\": int(year),\n",
+        "        },\n",
+        "        index=[0],\n",
+        "    ).round(2)"
+      ],
+      "id": "oojPqwSboiWU"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ti5ZXmbyn6Mm"
+      },
+      "outputs": [],
+      "source": [
+        "comparisons = []\n",
+        "area_est = []\n",
+        "for cropmap in TARGETS.values():\n",
+        "    cropmap, resolution, year = cropmap.title, cropmap.resolution, cropmap.year\n",
+        "    if cropmap not in gdf.columns:\n",
+        "        continue\n",
+        "    temp = gdf[[CLASS_COL, cropmap]].dropna()\n",
+        "    area = compute_area_estimate(cropmap, temp[CLASS_COL], temp[cropmap], a_j[cropmap], resolution, year)\n",
+        "    comparison = generate_report(cropmap, country, temp[CLASS_COL], temp[cropmap], a_j[cropmap], area_weighted=True)\n",
+        "    comparisons.append(comparison)\n",
+        "    area_est.append(area)\n",
+        "\n",
+        "comparisons = pd.concat(comparisons).set_index(['dataset'])\n",
+        "area_est = pd.concat(area_est).set_index(['dataset'])\n",
+        "\n",
+        "results = comparisons.merge(area_est, on='dataset')"
+      ],
+      "id": "ti5ZXmbyn6Mm"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "QrAgv7pP1lcz",
+        "outputId": "6f33c955-6ceb-4295-84ed-4aaf65c1512f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "                    \n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "                \n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "                    \n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "results.to_csv('results.csv')"
+      ],
+      "id": "QrAgv7pP1lcz"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "nAj0p7VS1_2K",
+        "outputId": "24843685-4f51-4e78-aaf5-1009f0413432"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                      crop_f1  accuracy  std_acc  crop_recall_pa  std_crop_pa  \\\n",
+              "dataset                                                                         \n",
+              "copernicus               0.52      0.86     0.01            0.49         0.05   \n",
+              "worldcover-v100          0.29      0.84     0.02            0.17         0.01   \n",
+              "glad                     0.56      0.89     0.01            0.47         0.05   \n",
+              "dynamicworld              NaN      0.00      NaN             NaN          NaN   \n",
+              "digital-earth-africa     0.54      0.88     0.01            0.52         0.03   \n",
+              "esri-lulc                0.55      0.89     0.01            0.42         0.03   \n",
+              "harvest-crop-maps        0.47      0.92     0.01            0.47         0.05   \n",
+              "\n",
+              "                      crop_precision_ua  std_crop_ua      area_ha      err_ha  \\\n",
+              "dataset                                                                         \n",
+              "copernicus                         0.54         0.04   8775032.13  1674917.82   \n",
+              "worldcover-v100                    0.91         0.04  11181466.37  1964935.17   \n",
+              "glad                               0.69         0.04   8504157.39  1587943.81   \n",
+              "dynamicworld                       0.00         0.00          NaN         NaN   \n",
+              "digital-earth-africa               0.56         0.04   8104337.54  1585223.55   \n",
+              "esri-lulc                          0.76         0.04   8957165.29  1635602.01   \n",
+              "harvest-crop-maps                  0.47         0.03   4536249.04  1366958.88   \n",
+              "\n",
+              "                      year  \n",
+              "dataset                     \n",
+              "copernicus            2019  \n",
+              "worldcover-v100       2020  \n",
+              "glad                  2019  \n",
+              "dynamicworld          2019  \n",
+              "digital-earth-africa  2019  \n",
+              "esri-lulc             2019  \n",
+              "harvest-crop-maps     2019  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5fe9c57a-3bf0-471e-b86f-27cb163a6c0e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>crop_f1</th>\n",
+              "      <th>accuracy</th>\n",
+              "      <th>std_acc</th>\n",
+              "      <th>crop_recall_pa</th>\n",
+              "      <th>std_crop_pa</th>\n",
+              "      <th>crop_precision_ua</th>\n",
+              "      <th>std_crop_ua</th>\n",
+              "      <th>area_ha</th>\n",
+              "      <th>err_ha</th>\n",
+              "      <th>year</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dataset</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>copernicus</th>\n",
+              "      <td>0.52</td>\n",
+              "      <td>0.86</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.49</td>\n",
+              "      <td>0.05</td>\n",
+              "      <td>0.54</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>8775032.13</td>\n",
+              "      <td>1674917.82</td>\n",
+              "      <td>2019</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>worldcover-v100</th>\n",
+              "      <td>0.29</td>\n",
+              "      <td>0.84</td>\n",
+              "      <td>0.02</td>\n",
+              "      <td>0.17</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.91</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>11181466.37</td>\n",
+              "      <td>1964935.17</td>\n",
+              "      <td>2020</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>glad</th>\n",
+              "      <td>0.56</td>\n",
+              "      <td>0.89</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.47</td>\n",
+              "      <td>0.05</td>\n",
+              "      <td>0.69</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>8504157.39</td>\n",
+              "      <td>1587943.81</td>\n",
+              "      <td>2019</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dynamicworld</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2019</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>digital-earth-africa</th>\n",
+              "      <td>0.54</td>\n",
+              "      <td>0.88</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.52</td>\n",
+              "      <td>0.03</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>8104337.54</td>\n",
+              "      <td>1585223.55</td>\n",
+              "      <td>2019</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>esri-lulc</th>\n",
+              "      <td>0.55</td>\n",
+              "      <td>0.89</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.42</td>\n",
+              "      <td>0.03</td>\n",
+              "      <td>0.76</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>8957165.29</td>\n",
+              "      <td>1635602.01</td>\n",
+              "      <td>2019</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>harvest-crop-maps</th>\n",
+              "      <td>0.47</td>\n",
+              "      <td>0.92</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.47</td>\n",
+              "      <td>0.05</td>\n",
+              "      <td>0.47</td>\n",
+              "      <td>0.03</td>\n",
+              "      <td>4536249.04</td>\n",
+              "      <td>1366958.88</td>\n",
+              "      <td>2019</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5fe9c57a-3bf0-471e-b86f-27cb163a6c0e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-5fe9c57a-3bf0-471e-b86f-27cb163a6c0e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-5fe9c57a-3bf0-471e-b86f-27cb163a6c0e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-ac11b8b2-e4f2-48f0-9672-12410996a46e\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ac11b8b2-e4f2-48f0-9672-12410996a46e')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-ac11b8b2-e4f2-48f0-9672-12410996a46e button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"results[['crop_f1','accuracy','std_acc','crop_recall_pa','std_crop_pa','crop_precision_ua','std_crop_ua','area_ha','err_ha', 'year']]\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"dataset\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"copernicus\",\n          \"worldcover-v100\",\n          \"esri-lulc\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"crop_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10225784403490362,\n        \"min\": 0.29,\n        \"max\": 0.56,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.52,\n          0.29,\n          0.47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33355944711835817,\n        \"min\": 0.0,\n        \"max\": 0.92,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.86,\n          0.84,\n          0.92\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_acc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00408248290463863,\n        \"min\": 0.01,\n        \"max\": 0.02,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.02,\n          0.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"crop_recall_pa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12832251036613437,\n        \"min\": 0.17,\n        \"max\": 0.52,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.17,\n          0.42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_crop_pa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.016329931618554522,\n        \"min\": 0.01,\n        \"max\": 0.05,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.05,\n          0.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"crop_precision_ua\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2891037052355995,\n        \"min\": 0.0,\n        \"max\": 0.91,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.54,\n          0.91\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_crop_ua\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.014960264830861913,\n        \"min\": 0.0,\n        \"max\": 0.04,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.04,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area_ha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2153835.017652397,\n        \"min\": 4536249.04,\n        \"max\": 11181466.37,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          8775032.13,\n          11181466.37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"err_ha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 193382.44049997826,\n        \"min\": 1366958.88,\n        \"max\": 1964935.17,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1674917.82,\n          1964935.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2019,\n        \"max\": 2020,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2020,\n          2019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ],
+      "source": [
+        "results[['crop_f1','accuracy','std_acc','crop_recall_pa','std_crop_pa','crop_precision_ua','std_crop_ua','area_ha','err_ha', 'year']]"
+      ],
+      "id": "nAj0p7VS1_2K"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results.dropna(inplace=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "qenOtnORfGTR",
+        "outputId": "fcd99cee-a1f5-4db0-bbe1-c6d918d65a53"
+      },
+      "id": "qenOtnORfGTR",
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "fraQjcTMpTwp",
+        "outputId": "914a654c-2f5c-42ea-c520-ae6467f32580"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x1400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "crop_proportion = round(gdf[CLASS_COL].value_counts(normalize=True)[1], 4) * 100\n",
+        "ax = results.sort_values(\"crop_f1\").plot(\n",
+        "    y=[\"accuracy\", \"crop_recall_pa\", \"crop_precision_ua\", \"crop_f1\"],\n",
+        "    xerr=\"std_crop_f1\",\n",
+        "    kind=\"barh\",\n",
+        "    figsize=(6, 14),\n",
+        "    width=0.8,\n",
+        "    title=f\"{country}: {len(gdf)} points (crop proportion: {crop_proportion}%)\",\n",
+        ");\n",
+        "\n",
+        "for c in ax.containers[1::2]:\n",
+        "    ax.bar_label(c)\n",
+        "\n",
+        "for border in [\"top\", \"right\", \"bottom\", \"left\"]:\n",
+        "    ax.spines[border].set_visible(False)\n",
+        "\n",
+        "ax.legend(bbox_to_anchor=(1, 1), reverse=True);"
+      ],
+      "id": "fraQjcTMpTwp"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Cropland Area estimates comparsion (FAOSTAT-cropland area included)"
+      ],
+      "metadata": {
+        "id": "ya4gJVqRQYPh"
+      },
+      "id": "ya4gJVqRQYPh"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fao_stat = pd.read_csv(\"./data/ref_samples_area/FAOSTAT_data_en_3-13-2024.csv\")\n",
+        "fao_stat = fao_stat[fao_stat['Area'] == country]\n",
+        "fao_stat = fao_stat[fao_stat['Year Code'] == reference_year]['Value'] * 1000\n",
+        "results = pd.concat([results, pd.DataFrame({'dataset':['FAOSTAT'], 'area_ha':fao_stat, 'err_ha':0, 'year':reference_year}).set_index(['dataset'])])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "L-nrhBekPfcp",
+        "outputId": "2e692893-dc70-49ec-dff0-5f6ce8d5b417"
+      },
+      "id": "L-nrhBekPfcp",
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "fig, ax = plt.subplots()\n",
+        "\n",
+        "n = len(results)\n",
+        "colors = plt.cm.viridis(np.linspace(0, 1, n))\n",
+        "\n",
+        "ax.barh(\n",
+        "    results.index,\n",
+        "    results[\"area_ha\"],\n",
+        "    xerr=results[\"err_ha\"],\n",
+        "    align=\"center\",\n",
+        "    alpha=0.5,\n",
+        "    ecolor=\"black\",\n",
+        "    color= colors\n",
+        ")\n",
+        "\n",
+        "ax.set_xticks(ax.get_xticks()); ax.set_yticks(ax.get_yticks())\n",
+        "ax.set_yticklabels([f\"{dataset} [{int(results.year[dataset])}]\" for dataset in results.index])\n",
+        "\n",
+        "for i, (value, err) in enumerate(zip(results[\"area_ha\"], results[\"err_ha\"])):\n",
+        "    ax.text(value, i, f\"{value:,} ± {err:,}\", ha=\"center\", va=\"bottom\")\n",
+        "ax.set_xlabel(\"Area (ha)\")\n",
+        "ax.set_ylabel('dataset')\n",
+        "ax.set_title(\"Area of cropland\")\n",
+        "ax.spines[\"right\"].set_visible(False)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "a0XEODxnBXW3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "b64e3e87-a749-4f63-88d5-e54cff82a8d4"
+      },
+      "id": "a0XEODxnBXW3",
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "gfTIBg6cwwAZ"
+      },
+      "id": "gfTIBg6cwwAZ",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.15"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/maps/Senegal_2022/Senegal_area_estimate.ipynb b/maps/Senegal_2022/Senegal_area_estimate.ipynb
new file mode 100644
index 00000000..1996a6bf
--- /dev/null
+++ b/maps/Senegal_2022/Senegal_area_estimate.ipynb
@@ -0,0 +1,2180 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "778ff440",
+      "metadata": {
+        "id": "778ff440"
+      },
+      "source": [
+        "# Intercomparison\n",
+        "\n",
+        "**Author:** Adebowale Adebayo\n",
+        "\n",
+        "**Last updated:** March 23, 2024\n",
+        "\n",
+        "**Description:** Runs intercomparison and **area estimate** for Senegal 2022\n",
+        "\n",
+        "## 1. Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fb42d13c",
+      "metadata": {
+        "id": "fb42d13c"
+      },
+      "outputs": [],
+      "source": [
+        "# !earthengine authenticate"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "id": "hZ8qzSlB75kl",
+      "metadata": {
+        "id": "hZ8qzSlB75kl",
+        "outputId": "ab136daf-c3e3-44ea-9257-26d60d10053a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 160
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'crop-mask'...\n",
+            "remote: Enumerating objects: 12171, done.\u001b[K\n",
+            "remote: Counting objects: 100% (1603/1603), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (441/441), done.\u001b[K\n",
+            "remote: Total 12171 (delta 1209), reused 1380 (delta 1135), pack-reused 10568\u001b[K\n",
+            "Receiving objects: 100% (12171/12171), 125.89 MiB | 11.33 MiB/s, done.\n",
+            "Resolving deltas: 100% (7902/7902), done.\n",
+            "Updating files: 100% (212/212), done.\n"
+          ]
+        }
+      ],
+      "source": [
+        "!git clone https://github.com/nasaharvest/crop-mask.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 71,
+      "id": "1fe-6D3f8LTb",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "1fe-6D3f8LTb",
+        "outputId": "39e61b33-f367-42a0-e115-1e4e61d322ab"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/crop-mask/crop-mask\n"
+          ]
+        }
+      ],
+      "source": [
+        "%cd crop-mask/"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "V6lTs8Z9Pt-T",
+      "metadata": {
+        "id": "V6lTs8Z9Pt-T"
+      },
+      "outputs": [],
+      "source": [
+        "!git checkout area-estimate-from-multi-land-cover"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "id": "gEUyxHk9MEU2",
+      "metadata": {
+        "id": "gEUyxHk9MEU2",
+        "outputId": "5d4a0b49-6f3e-452b-8865-139a7775b11e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "!pip install cartopy -qq\n",
+        "!pip install rasterio -qq\n",
+        "!pip install dvc[gs] -qq"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 74,
+      "id": "9907f9a5",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "9907f9a5",
+        "outputId": "593b90ea-fe30-4b14-9a78-ae61a142e9a9"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import ee\n",
+        "import geemap\n",
+        "import sys\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "import geopandas as gpd\n",
+        "from pathlib import Path\n",
+        "\n",
+        "ee.Authenticate()\n",
+        "ee.Initialize(project=\"bsos-geog-harvest1\")\n",
+        "\n",
+        "sys.path.append(\"../..\")\n",
+        "\n",
+        "from src.compare_covermaps import TARGETS, filter_by_bounds, generate_report, CLASS_COL, COUNTRY_COL, get_ensemble_area\n",
+        "from src.compare_covermaps import TEST_COUNTRIES, TEST_CODE"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c61ea4f8",
+      "metadata": {
+        "id": "c61ea4f8"
+      },
+      "source": [
+        "## 2. Read in evaluation set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 75,
+      "id": "7f75e567",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "7f75e567",
+        "outputId": "2dea5488-5790-4ec2-f2cf-35e17ac6cda8"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "country = \"Senegal\"\n",
+        "\n",
+        "if country not in TEST_CODE:\n",
+        "    print(f\"WARNING: {country} not found in TEST_CODE in src/compare_covermaps.py\")\n",
+        "if country not in TEST_COUNTRIES:\n",
+        "    print(f\"WARNING: {country} not found in TEST_COUNTRIES in src/compare_covermaps.py\")\n",
+        "if country not in TEST_CODE or country not in TEST_COUNTRIES:\n",
+        "    print(\"Please update src/compare_covermaps.py and restart the notebook.\")\n",
+        "else:\n",
+        "    country_code = TEST_CODE[country]\n",
+        "    # dataset_path = \"../\" + TEST_COUNTRIES[country]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "prvHkUXTOe7P",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "prvHkUXTOe7P",
+        "outputId": "2bcc021f-673d-4178-be56-1a9608dad321"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# dataset_path = TEST_COUNTRIES[country]\n",
+        "dataset_path = 'data/datasets/Senegal_CEO_2022.csv'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "vbVX8gFd_N3J",
+      "metadata": {
+        "id": "vbVX8gFd_N3J"
+      },
+      "outputs": [],
+      "source": [
+        "!dvc pull data/datasets"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "V8XeT-qci7VG",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "V8XeT-qci7VG",
+        "outputId": "c109753f-d46d-465d-8080-295c1783661e"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "if not Path(dataset_path).exists():\n",
+        "    print(f\"WARNING: Dataset: {dataset_path} not found, run `dvc pull data/datasets from root.\")\n",
+        "else:\n",
+        "    df = pd.read_csv(dataset_path)[[\"lat\", \"lon\", \"class_probability\", \"subset\"]]\n",
+        "    df = df[(df[\"class_probability\"] != 0.5)].copy()\n",
+        "    # use only test data because validation points used for harvest-dev map\n",
+        "    df = df[(df[\"subset\"] == \"validation\") | (df[\"subset\"] == \"testing\")].copy()\n",
+        "    df[CLASS_COL] = (df[\"class_probability\"] > 0.5).astype(int)\n",
+        "    df[COUNTRY_COL] = country\n",
+        "\n",
+        "    gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.lon, df.lat), crs=\"epsg:4326\")\n",
+        "    gdf = filter_by_bounds(country_code=country_code, gdf=gdf)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "31341d98",
+      "metadata": {
+        "id": "31341d98"
+      },
+      "source": [
+        "## 3. Run intercomparison"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ImkKe6cEB4aB",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "ImkKe6cEB4aB",
+        "outputId": "b944d6a8-e92c-4e2d-9fad-6d4ee5d257f6"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         lat        lon  class_probability      subset  binary  country  \\\n",
+              "0  15.033306 -16.937735           0.000000     testing       0  Senegal   \n",
+              "2  16.192133 -14.772795           0.000000  validation       0  Senegal   \n",
+              "3  15.015340 -13.173794           0.000000  validation       0  Senegal   \n",
+              "4  14.799744 -15.329750           0.000000     testing       0  Senegal   \n",
+              "5  14.260755 -14.656014           0.333333     testing       0  Senegal   \n",
+              "\n",
+              "                     geometry  \n",
+              "0  POINT (-16.93773 15.03331)  \n",
+              "2  POINT (-14.77279 16.19213)  \n",
+              "3  POINT (-13.17379 15.01534)  \n",
+              "4  POINT (-15.32975 14.79974)  \n",
+              "5  POINT (-14.65601 14.26076)  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-74e9a31d-ed4f-482e-8ee3-42955557fa25\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>lat</th>\n",
+              "      <th>lon</th>\n",
+              "      <th>class_probability</th>\n",
+              "      <th>subset</th>\n",
+              "      <th>binary</th>\n",
+              "      <th>country</th>\n",
+              "      <th>geometry</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>15.033306</td>\n",
+              "      <td>-16.937735</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>testing</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Senegal</td>\n",
+              "      <td>POINT (-16.93773 15.03331)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>16.192133</td>\n",
+              "      <td>-14.772795</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>validation</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Senegal</td>\n",
+              "      <td>POINT (-14.77279 16.19213)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>15.015340</td>\n",
+              "      <td>-13.173794</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>validation</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Senegal</td>\n",
+              "      <td>POINT (-13.17379 15.01534)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>14.799744</td>\n",
+              "      <td>-15.329750</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>testing</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Senegal</td>\n",
+              "      <td>POINT (-15.32975 14.79974)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>14.260755</td>\n",
+              "      <td>-14.656014</td>\n",
+              "      <td>0.333333</td>\n",
+              "      <td>testing</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Senegal</td>\n",
+              "      <td>POINT (-14.65601 14.26076)</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-74e9a31d-ed4f-482e-8ee3-42955557fa25')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-74e9a31d-ed4f-482e-8ee3-42955557fa25 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-74e9a31d-ed4f-482e-8ee3-42955557fa25');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-3aed5f31-cd99-4c14-91fc-cdd4f6219209\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3aed5f31-cd99-4c14-91fc-cdd4f6219209')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-3aed5f31-cd99-4c14-91fc-cdd4f6219209 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "gdf",
+              "summary": "{\n  \"name\": \"gdf\",\n  \"rows\": 1174,\n  \"fields\": [\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1419422148869118,\n        \"min\": 12.33836043,\n        \"max\": 16.64129064,\n        \"num_unique_values\": 414,\n        \"samples\": [\n          13.33549039,\n          12.76955176,\n          12.84141699\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.398664516536919,\n        \"min\": -17.17129666,\n        \"max\": -11.39512938,\n        \"num_unique_values\": 527,\n        \"samples\": [\n          -14.16194045,\n          -14.35058666,\n          -15.29381771\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2867099180404072,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.3333333333333333,\n          0.6666666666666666,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"subset\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"validation\",\n          \"testing\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Senegal\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geometry\",\n      \"properties\": {\n        \"dtype\": \"geometry\",\n        \"num_unique_values\": 1174,\n        \"samples\": [\n          \"POINT (-16.52450965 14.92550845)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 69
+        }
+      ],
+      "source": [
+        "gdf.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "54c4cc0f",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "54c4cc0f",
+        "outputId": "5d6bfbea-8783-4736-b85a-7573a83be4c2"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "TARGETS = {k:v for k,v in TARGETS.items()}\n",
+        "for k, v in TARGETS.items():\n",
+        "    if country not in v.countries:\n",
+        "        continue\n",
+        "    if v.year is None:\n",
+        "        v.year = v.collection_years[v.countries.index(country)]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1oQjubrHjkBi",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "1oQjubrHjkBi",
+        "outputId": "da49f633-e609-4cc5-c946-24d0a28c153d"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "reference_year = 2022\n",
+        "TARGETS = {k: v for k, v in TARGETS.items() if v.year in range(reference_year - 2, reference_year + 2)} # Adjusted the year range to include more map products\n",
+        "# TARGETS = {k: v for k, v in TARGETS.items() if v.year in [reference_year - 2, reference_year, reference_year + 2]}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "98e241d2",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 71
+        },
+        "id": "98e241d2",
+        "outputId": "613d619e-b08c-4b3a-df2d-4719b35c4fb9"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[Senegal] sampling worldcover-v100...\n",
+            "[Senegal] sampling worldcover-v200...\n",
+            "[Senegal] sampling worldcereal-v100...\n"
+          ]
+        }
+      ],
+      "source": [
+        "for cropmap in TARGETS.values():\n",
+        "    if country not in cropmap.countries:\n",
+        "        continue\n",
+        "    print(f\"[{country}] sampling \" + cropmap.title + \"...\")\n",
+        "    map_sampled = cropmap.extract_test(gdf).copy()\n",
+        "    gdf = pd.merge(gdf, map_sampled, on=[\"lat\", \"lon\"], how=\"left\")\n",
+        "    gdf.drop_duplicates(inplace=True)  # TODO find why points get duplicated"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "95a0f536",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 71
+        },
+        "id": "95a0f536",
+        "outputId": "b2c3ab38-6f4f-4e6a-a838-8b5c3d50326c"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[Senegal] calculating pixel area for worldcover-v100...\n",
+            "[Senegal] calculating pixel area for worldcover-v200...\n",
+            "[Senegal] calculating pixel area for worldcereal-v100...\n"
+          ]
+        }
+      ],
+      "source": [
+        "a_j = {}\n",
+        "for cropmap in TARGETS.values():\n",
+        "    if country not in cropmap.countries:\n",
+        "        continue\n",
+        "    print(f\"[{country}] calculating pixel area for \" + cropmap.title + \"...\")\n",
+        "    # a_j[cropmap.title] = cropmap.compute_map_area(country, export=True, dataset_name=cropmap.title).copy() # I already have the export map areas\n",
+        "    a_j[cropmap.title] = np.array([None,None])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5fJPzvOeUo9G",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "5fJPzvOeUo9G",
+        "outputId": "03eeee33-19b5-45fc-8df2-5feb8aade1f2"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# update a_j values with exported values\n",
+        "for cropmap in a_j.keys():\n",
+        "    try:\n",
+        "        area_df = pd.read_csv(f'./Crop_NonCrop_Area_Sum_Export-{country}-{cropmap}.csv')\n",
+        "    except:\n",
+        "        continue\n",
+        "    crop_area = int(area_df['crop_sum'][0])\n",
+        "    noncrop_area = int(area_df['noncrop_sum'][0])\n",
+        "    a_j[cropmap] = np.array([noncrop_area, crop_area])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "zyR4qCJ49Rh5",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "zyR4qCJ49Rh5",
+        "outputId": "e4d11d48-53e2-4a9e-bd99-72efb379d0a5"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Change None to nan\n",
+        "a_j = {k: np.array([np.nan, np.nan]) if np.any(v == None) else v for k,v in a_j.items()}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "LY6Q_QtUgME_",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "LY6Q_QtUgME_",
+        "outputId": "691142c7-c26c-4eb7-e91c-7ac4ce59b000"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from src.area_utils import compute_area_estimate, compute_area_error_matrix, compute_std_p_i\n",
+        "from sklearn.metrics import confusion_matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "oojPqwSboiWU",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "oojPqwSboiWU",
+        "outputId": "f83877e9-55a7-43d3-ec9f-8e30752f0c69"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def compute_area_estimate(dataset, true, pred, a_j, resolution, year):\n",
+        "    cm = confusion_matrix(true, pred)\n",
+        "    total_px = a_j.sum()\n",
+        "    w_j = a_j / total_px\n",
+        "\n",
+        "    am = compute_area_error_matrix(cm, w_j)\n",
+        "    a_i = am.sum(axis=1)\n",
+        "    std_a_i = compute_std_p_i(w_j, am, cm)\n",
+        "    err_a_i = 1.96 * std_a_i\n",
+        "\n",
+        "    a_px = total_px * a_i\n",
+        "    err_px = err_a_i * total_px\n",
+        "    return pd.DataFrame(\n",
+        "        data={\n",
+        "            \"dataset\": dataset,\n",
+        "            \"area_ha\": a_px[1] * (resolution**2) / (100**2),\n",
+        "            \"err_ha\": err_px[1] * (resolution**2) / (100**2),\n",
+        "            \"year\": int(year),\n",
+        "        },\n",
+        "        index=[0],\n",
+        "    ).round(2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ti5ZXmbyn6Mm",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "ti5ZXmbyn6Mm",
+        "outputId": "9a1da855-5908-46bf-c0be-82945f64c788"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "comparisons = []\n",
+        "area_est = []\n",
+        "for cropmap in TARGETS.values():\n",
+        "    cropmap, resolution, year = cropmap.title, cropmap.resolution, cropmap.year\n",
+        "    if cropmap not in gdf.columns:\n",
+        "        continue\n",
+        "    temp = gdf[[CLASS_COL, cropmap]].dropna()\n",
+        "    area = compute_area_estimate(cropmap, temp[CLASS_COL], temp[cropmap], a_j[cropmap], resolution, year)\n",
+        "    comparison = generate_report(cropmap, country, temp[CLASS_COL], temp[cropmap], a_j[cropmap], area_weighted=True)\n",
+        "    comparisons.append(comparison)\n",
+        "    area_est.append(area)\n",
+        "\n",
+        "# # Add ensemble\n",
+        "# ensemble_maps = [\"glad\", \"esri-lulc\"] # Should be odd number\n",
+        "\n",
+        "# print(f\"Ensemble maps: {ensemble_maps}\")\n",
+        "# ensemble = gdf[ensemble_maps].mode(axis='columns')\n",
+        "# a_j['ensemble-subset'] = get_ensemble_area(country, [TARGETS[name] for name in ensemble_maps])\n",
+        "# comparison = generate_report(\"ensemble-subset\", country, gdf[CLASS_COL], ensemble, a_j['ensemble-subset'], area_weighted=True)\n",
+        "# area = compute_area_estimate(\"ensemble-subset\", gdf[CLASS_COL], ensemble, a_j['ensemble-subset'], 10)\n",
+        "# comparisons.append(comparison)\n",
+        "# area_est.append(area)\n",
+        "\n",
+        "comparisons = pd.concat(comparisons).set_index(['dataset'])\n",
+        "area_est = pd.concat(area_est).set_index(['dataset'])\n",
+        "\n",
+        "results = comparisons.merge(area_est, on='dataset')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "QrAgv7pP1lcz",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "QrAgv7pP1lcz",
+        "outputId": "6f33c955-6ceb-4295-84ed-4aaf65c1512f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "                    \n",
+              "                .geemap-dark .jupyter-button {\n",
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+              "                \n",
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+              "                    background-color: var(--colab-primary-surface-color, white);\n",
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+              "                    \n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
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+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "results.to_csv('results.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "nAj0p7VS1_2K",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 175
+        },
+        "id": "nAj0p7VS1_2K",
+        "outputId": "b3fe8257-7e90-43e7-cecf-095c2d328aa8"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
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+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  crop_f1  accuracy  std_acc  crop_recall_pa  std_crop_pa  \\\n",
+              "dataset                                                                     \n",
+              "worldcover-v100      0.65      0.89     0.01            0.70         0.03   \n",
+              "worldcover-v200      0.67      0.90     0.01            0.73         0.03   \n",
+              "worldcereal-v100     0.64      0.89     0.01            0.68         0.03   \n",
+              "\n",
+              "                  crop_precision_ua  std_crop_ua     area_ha     err_ha  year  \n",
+              "dataset                                                                        \n",
+              "worldcover-v100                0.62         0.04  2991154.22  343812.58  2020  \n",
+              "worldcover-v200                0.63         0.04  2974111.70  334988.16  2021  \n",
+              "worldcereal-v100               0.61         0.04  3013651.69  351020.07  2021  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a5893ae4-54cd-40db-8786-2f936165fe2a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "    .dataframe thead th {\n",
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+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>crop_f1</th>\n",
+              "      <th>accuracy</th>\n",
+              "      <th>std_acc</th>\n",
+              "      <th>crop_recall_pa</th>\n",
+              "      <th>std_crop_pa</th>\n",
+              "      <th>crop_precision_ua</th>\n",
+              "      <th>std_crop_ua</th>\n",
+              "      <th>area_ha</th>\n",
+              "      <th>err_ha</th>\n",
+              "      <th>year</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dataset</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>worldcover-v100</th>\n",
+              "      <td>0.65</td>\n",
+              "      <td>0.89</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.70</td>\n",
+              "      <td>0.03</td>\n",
+              "      <td>0.62</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>2991154.22</td>\n",
+              "      <td>343812.58</td>\n",
+              "      <td>2020</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>worldcover-v200</th>\n",
+              "      <td>0.67</td>\n",
+              "      <td>0.90</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.73</td>\n",
+              "      <td>0.03</td>\n",
+              "      <td>0.63</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>2974111.70</td>\n",
+              "      <td>334988.16</td>\n",
+              "      <td>2021</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>worldcereal-v100</th>\n",
+              "      <td>0.64</td>\n",
+              "      <td>0.89</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.68</td>\n",
+              "      <td>0.03</td>\n",
+              "      <td>0.61</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>3013651.69</td>\n",
+              "      <td>351020.07</td>\n",
+              "      <td>2021</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "        document.querySelector('#df-a5893ae4-54cd-40db-8786-2f936165fe2a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a5893ae4-54cd-40db-8786-2f936165fe2a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
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+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-8b499663-4b89-4764-a17d-cecbf1aadd99 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"results[['crop_f1','accuracy','std_acc','crop_recall_pa','std_crop_pa','crop_precision_ua','std_crop_ua','area_ha','err_ha', 'year']] # include User and producer's accuracy with their 95% CI\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"dataset\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"worldcover-v100\",\n          \"worldcover-v200\",\n          \"worldcereal-v100\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"crop_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01527525231651948,\n        \"min\": 0.64,\n        \"max\": 0.67,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.65,\n          0.67,\n          0.64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005773502691896263,\n        \"min\": 0.89,\n        \"max\": 0.9,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9,\n          0.89\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_acc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.01,\n        \"max\": 0.01,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"crop_recall_pa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.025166114784235805,\n        \"min\": 0.68,\n        \"max\": 0.73,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_crop_pa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.03,\n        \"max\": 0.03,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.03\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"crop_precision_ua\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010000000000000009,\n        \"min\": 0.61,\n        \"max\": 0.63,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_crop_ua\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.04,\n        \"max\": 0.04,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area_ha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19832.60973561544,\n        \"min\": 2974111.7,\n        \"max\": 3013651.69,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2991154.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"err_ha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8029.533389147442,\n        \"min\": 334988.16,\n        \"max\": 351020.07,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          343812.58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2020,\n        \"max\": 2021,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 58
+        }
+      ],
+      "source": [
+        "results[['crop_f1','accuracy','std_acc','crop_recall_pa','std_crop_pa','crop_precision_ua','std_crop_ua','area_ha','err_ha', 'year']] # include User and producer's accuracy with their 95% CI"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fa969373",
+      "metadata": {
+        "id": "fa969373"
+      },
+      "source": [
+        "## 4. Visualize best available map"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "qenOtnORfGTR",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "qenOtnORfGTR",
+        "outputId": "b1db1068-4c95-45f5-bf5a-9c3603a49c1d"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "results.dropna(inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fraQjcTMpTwp",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "fraQjcTMpTwp",
+        "outputId": "c0189358-63bf-4109-e863-557d3af39789"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x1400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "crop_proportion = round(gdf[CLASS_COL].value_counts(normalize=True)[1], 4) * 100\n",
+        "ax = results.sort_values(\"crop_f1\").plot(\n",
+        "    y=[\"accuracy\", \"crop_recall_pa\", \"crop_precision_ua\", \"crop_f1\"],\n",
+        "    xerr=\"std_crop_f1\",\n",
+        "    kind=\"barh\",\n",
+        "    figsize=(6, 14),\n",
+        "    width=0.8,\n",
+        "    title=f\"{country}: {len(gdf)} points (crop proportion: {crop_proportion}%)\",\n",
+        ");\n",
+        "\n",
+        "for c in ax.containers[1::2]:\n",
+        "    ax.bar_label(c)\n",
+        "\n",
+        "for border in [\"top\", \"right\", \"bottom\", \"left\"]:\n",
+        "    ax.spines[border].set_visible(False)\n",
+        "\n",
+        "ax.legend(bbox_to_anchor=(1, 1), reverse=True);"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "L-nrhBekPfcp",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "L-nrhBekPfcp",
+        "outputId": "0d05922f-af8a-4bf2-f03d-d01caeb45e73"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fao_stat = pd.read_csv(\"./data/ref_samples_area/FAOSTAT_data_en_3-13-2024.csv\")\n",
+        "fao_stat = fao_stat[fao_stat['Area'] == country]\n",
+        "fao_stat = fao_stat[fao_stat['Year'] == 2021]['Value'] * 1000 # Using 2021, no data for 2022\n",
+        "results = pd.concat([results, pd.DataFrame({'dataset':['FAOSTAT'], 'area_ha':fao_stat, 'err_ha':0, 'year':2021}).set_index(['dataset'])])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 76,
+      "id": "a0XEODxnBXW3",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "a0XEODxnBXW3",
+        "outputId": "3224743f-4d61-4603-9441-3ec098bc90c3"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <style>\n",
+              "                .geemap-dark {\n",
+              "                    --jp-widgets-color: white;\n",
+              "                    --jp-widgets-label-color: white;\n",
+              "                    --jp-ui-font-color1: white;\n",
+              "                    --jp-layout-color2: #454545;\n",
+              "                    background-color: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-dark .jupyter-button {\n",
+              "                    --jp-layout-color3: #383838;\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab {\n",
+              "                    background-color: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "\n",
+              "                .geemap-colab .jupyter-button {\n",
+              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+              "                }\n",
+              "            </style>\n",
+              "            "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "fig, ax = plt.subplots()\n",
+        "\n",
+        "n = len(results)\n",
+        "colors = plt.cm.viridis(np.linspace(0, 1, n))\n",
+        "\n",
+        "ax.barh(\n",
+        "    results.index,\n",
+        "    results[\"area_ha\"],\n",
+        "    xerr=results[\"err_ha\"],\n",
+        "    align=\"center\",\n",
+        "    alpha=0.5,\n",
+        "    ecolor=\"black\",\n",
+        "    color= colors\n",
+        ")\n",
+        "\n",
+        "ax.set_xticks(ax.get_xticks()); ax.set_yticks(ax.get_yticks())\n",
+        "ax.set_yticklabels([f\"{dataset} ({int(results.year[dataset])})\" for dataset in results.index])\n",
+        "\n",
+        "for i, (value, err) in enumerate(zip(results[\"area_ha\"], results[\"err_ha\"])):\n",
+        "    ax.text(value, i, f\"{value:,} ± {err:,}\", ha=\"center\", va=\"bottom\")\n",
+        "ax.set_xlabel(\"Area (ha)\")\n",
+        "ax.set_ylabel(\"dataset\")\n",
+        "ax.set_title(\"Area of cropland\")\n",
+        "ax.spines[\"right\"].set_visible(False)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "vedCX18xNNR-"
+      },
+      "id": "vedCX18xNNR-",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.15"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/maps/_templates/area_estimate_intercomparison.ipynb b/maps/_templates/area_estimate_intercomparison.ipynb
new file mode 100644
index 00000000..7beebdec
--- /dev/null
+++ b/maps/_templates/area_estimate_intercomparison.ipynb
@@ -0,0 +1,644 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "778ff440",
+      "metadata": {
+        "id": "778ff440"
+      },
+      "source": [
+        "# Intercomparison\n",
+        "\n",
+        "**Author:**\n",
+        "\n",
+        "**Last updated:**\n",
+        "\n",
+        "**Description:** Runs intercomparison for [Country Year]\n",
+        "\n",
+        "## 1. Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fb42d13c",
+      "metadata": {
+        "id": "fb42d13c"
+      },
+      "outputs": [],
+      "source": [
+        "# !earthengine authenticate"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "hZ8qzSlB75kl",
+      "metadata": {
+        "id": "hZ8qzSlB75kl"
+      },
+      "outputs": [],
+      "source": [
+        "!git clone https://github.com/nasaharvest/crop-mask.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d25d6ff7",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install cartopy -qq\n",
+        "!pip install rasterio -qq\n",
+        "!pip install dvc[gs] -qq"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9907f9a5",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "9907f9a5",
+        "outputId": "c9bf6e76-a345-4f21-873c-f804bc9465f4"
+      },
+      "outputs": [],
+      "source": [
+        "import ee\n",
+        "import geemap\n",
+        "import sys\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "import geopandas as gpd\n",
+        "from pathlib import Path\n",
+        "\n",
+        "ee.Authenticate()\n",
+        "ee.Initialize(project=\"bsos-geog-harvest1\")\n",
+        "\n",
+        "sys.path.append(\"../..\")\n",
+        "\n",
+        "from src.compare_covermaps import TARGETS, filter_by_bounds, generate_report, CLASS_COL, COUNTRY_COL, get_ensemble_area\n",
+        "from src.compare_covermaps import TEST_COUNTRIES, TEST_CODE"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c61ea4f8",
+      "metadata": {
+        "id": "c61ea4f8"
+      },
+      "source": [
+        "## 2. Read in evaluation set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7f75e567",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "7f75e567",
+        "outputId": "6bc628a4-b5b9-46f7-deae-4e19bd4cfc7e"
+      },
+      "outputs": [],
+      "source": [
+        "country = \"<COUNTRY STRING GOES HERE>\"\n",
+        "\n",
+        "if country not in TEST_CODE:\n",
+        "    print(f\"WARNING: {country} not found in TEST_CODE in src/compare_covermaps.py\")\n",
+        "if country not in TEST_COUNTRIES:\n",
+        "    print(f\"WARNING: {country} not found in TEST_COUNTRIES in src/compare_covermaps.py\")\n",
+        "if country not in TEST_CODE or country not in TEST_COUNTRIES:\n",
+        "    print(\"Please update src/compare_covermaps.py and restart the notebook.\")\n",
+        "else:\n",
+        "    country_code = TEST_CODE[country]\n",
+        "    # dataset_path = \"../\" + TEST_COUNTRIES[country]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "vbVX8gFd_N3J",
+      "metadata": {
+        "id": "vbVX8gFd_N3J"
+      },
+      "outputs": [],
+      "source": [
+        "# !dvc pull data/datasets"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "df7a7aaf",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ceo_set1 = \"<PATH TO CEO REFERENCE SAMPLE SET1>\"\n",
+        "ceo_set2 = \"<PATH TO CEO REFERENCE SAMPLE SET2>\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c2c6cea9",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def reference_sample_agree(ceo_ref1, ceo_ref2):\n",
+        "    ceo_ref1 = pd.read_csv(ceo_ref1)\n",
+        "    ceo_ref2 = pd.read_csv(ceo_ref2)\n",
+        "\n",
+        "    assert ceo_ref1.columns[-1] == ceo_ref2.columns[-1]\n",
+        "\n",
+        "    label_question = ceo_ref1.columns[-1]\n",
+        "\n",
+        "    print(f\"Number of NANs/ missing answers in set 1: {ceo_ref1[label_question].isna().sum()}\")\n",
+        "    print(f\"Number of NANs/ missing answers in set 2: {ceo_ref2[label_question].isna().sum()}\")\n",
+        "\n",
+        "    if ceo_ref1.shape[0] != ceo_ref2.shape[0]:\n",
+        "        print(\"The number of rows in the reference sets are not equal.\")\n",
+        "        print(\"Checking for duplictes on 'plotid'..\")\n",
+        "        print(\n",
+        "            \" Number of duplicated in set 1: %s\" % ceo_ref1[ceo_ref1.plotid.duplicated()].shape[0]\n",
+        "        )\n",
+        "        print(\n",
+        "            \" Number of duplicated in set 2: %s\" % ceo_ref2[ceo_ref2.plotid.duplicated()].shape[0]\n",
+        "        )\n",
+        "        print(\"Removing duplicates and keeping the first...\")\n",
+        "        ceo_ref1 = ceo_ref1.drop_duplicates(subset=\"plotid\", keep=\"first\")\n",
+        "        ceo_ref2 = ceo_ref2.drop_duplicates(subset=\"plotid\", keep=\"first\")\n",
+        "\n",
+        "        ceo_ref1.set_index(\"plotid\", inplace=True)\n",
+        "        ceo_ref2.set_index(\"plotid\", inplace=True)\n",
+        "    else:\n",
+        "        print(\"The number of rows in the reference sets are equal.\")\n",
+        "\n",
+        "    ceo_agree = ceo_ref1[ceo_ref1[label_question] == ceo_ref2[label_question]]\n",
+        "\n",
+        "    print(\n",
+        "        \"Number of samples that are in agreement: %d out of %d (%.2f%%)\"\n",
+        "        % (\n",
+        "            ceo_agree.shape[0],\n",
+        "            ceo_ref1.shape[0],\n",
+        "            ceo_agree.shape[0] / ceo_ref1.shape[0] * 100,\n",
+        "        )\n",
+        "    )\n",
+        "    ceo_agree_geom = gpd.GeoDataFrame(\n",
+        "        ceo_agree,\n",
+        "        geometry=gpd.points_from_xy(ceo_agree.lon, ceo_agree.lat),\n",
+        "        crs=\"EPSG:4326\",\n",
+        "    )\n",
+        "\n",
+        "    label_responses = ceo_agree_geom[label_question].unique()\n",
+        "    assert len(label_responses) == 2\n",
+        "\n",
+        "    for r, row in ceo_agree_geom.iterrows():\n",
+        "\n",
+        "        try:\n",
+        "            if (\n",
+        "                row[label_question].lower() == \"crop\"\n",
+        "                or row[label_question].lower() == \"cropland\"\n",
+        "                or row[label_question].lower() == \"planted\"\n",
+        "            ):\n",
+        "                ceo_agree_geom.loc[r, CLASS_COL] = 1\n",
+        "            elif(\n",
+        "                row[label_question].lower() == \"non-crop\"\n",
+        "                or row[label_question].lower() == \"non-cropland\"\n",
+        "                or row[label_question].lower() == \"not planted\"\n",
+        "            ):\n",
+        "                ceo_agree_geom.loc[r, CLASS_COL] = 0\n",
+        "        except IndexError:\n",
+        "            ceo_agree_geom.loc[r, CLASS_COL] = 255\n",
+        "    \n",
+        "    ceo_agree_geom = ceo_agree_geom[ceo_agree_geom[CLASS_COL] != 255]\n",
+        "\n",
+        "    ceo_agree_geom[CLASS_COL] = ceo_agree_geom[CLASS_COL].astype(int)\n",
+        "    ceo_agree_geom[COUNTRY_COL] = country\n",
+        "    ceo_agree_geom = ceo_agree_geom[['lat','lon',CLASS_COL, COUNTRY_COL, 'geometry']]\n",
+        "    \n",
+        "    return ceo_agree_geom"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6745bf0d",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "gdf = reference_sample_agree(ceo_set1,ceo_set2)\n",
+        "gdf = filter_by_bounds(country_code=country_code, gdf=gdf)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2d313baa",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "2d313baa",
+        "outputId": "82eca2f6-b19d-4f48-c8f8-8789b671fb89"
+      },
+      "outputs": [],
+      "source": [
+        "# if not Path(dataset_path).exists():\n",
+        "#     print(f\"WARNING: Dataset: {dataset_path} not found, run `dvc pull data/datasets from root.\")\n",
+        "# else:\n",
+        "#     df = pd.read_csv(dataset_path)[[\"lat\", \"lon\", \"class_probability\", \"subset\"]]\n",
+        "#     df = df[(df[\"class_probability\"] != 0.5)].copy()\n",
+        "#     # use only test data because validation points used for harvest-dev map\n",
+        "#     df = df[df[\"subset\"] == \"testing\"].copy()\n",
+        "#     df[CLASS_COL] = (df[\"class_probability\"] > 0.5).astype(int)\n",
+        "#     df[COUNTRY_COL] = country\n",
+        "\n",
+        "#     gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.lon, df.lat), crs=\"epsg:4326\")\n",
+        "#     gdf = filter_by_bounds(country_code=country_code, gdf=gdf)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "31341d98",
+      "metadata": {
+        "id": "31341d98"
+      },
+      "source": [
+        "## 3. Run intercomparison"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ImkKe6cEB4aB",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "ImkKe6cEB4aB",
+        "outputId": "1735fb12-fff5-4db1-e6c7-5ec405510063"
+      },
+      "outputs": [],
+      "source": [
+        "gdf.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "54c4cc0f",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "54c4cc0f",
+        "outputId": "9c0dde81-9fe7-4b95-ed84-cf2e843a8bbb"
+      },
+      "outputs": [],
+      "source": [
+        "TARGETS = {k:v for k,v in TARGETS.items()}\n",
+        "for k, v in TARGETS.items():\n",
+        "    if country not in v.countries:\n",
+        "        continue\n",
+        "    if v.year is None:\n",
+        "        v.year = v.collection_years[v.countries.index(country)]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1oQjubrHjkBi",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "1oQjubrHjkBi",
+        "outputId": "281b6668-e99c-420a-cdf9-dd6a6305b67c"
+      },
+      "outputs": [],
+      "source": [
+        "reference_year = \"<YEAR INTEGER GOES HERE>\"\n",
+        "TARGETS = {k: v for k, v in TARGETS.items() if v.year in [reference_year - 1, reference_year, reference_year + 1]}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "98e241d2",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "id": "98e241d2",
+        "outputId": "f4eac0c8-86d5-4145-fb60-e82795da095c"
+      },
+      "outputs": [],
+      "source": [
+        "for cropmap in TARGETS.values():\n",
+        "    if country not in cropmap.countries:\n",
+        "        continue\n",
+        "    print(f\"[{country}] sampling \" + cropmap.title + \"...\")\n",
+        "    map_sampled = cropmap.extract_test(gdf).copy()\n",
+        "    gdf = pd.merge(gdf, map_sampled, on=[\"lat\", \"lon\"], how=\"left\")\n",
+        "    gdf.drop_duplicates(inplace=True)  # TODO find why points get duplicated"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "95a0f536",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 267
+        },
+        "id": "95a0f536",
+        "outputId": "194c8876-85b9-4c4b-e96f-14135eb83efc"
+      },
+      "outputs": [],
+      "source": [
+        "a_j = {}\n",
+        "for cropmap in TARGETS.values():\n",
+        "    if country not in cropmap.countries:\n",
+        "        continue\n",
+        "    print(f\"[{country}] calculating pixel area for \" + cropmap.title + \"...\")\n",
+        "    a_j[cropmap.title] = cropmap.compute_map_area(country, export=True, dataset_name=cropmap.title).copy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5fJPzvOeUo9G",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "5fJPzvOeUo9G",
+        "outputId": "ca430174-f6b7-4165-e05d-b60bea834408"
+      },
+      "outputs": [],
+      "source": [
+        "# update a_j values with exported values\n",
+        "for cropmap in a_j.keys():\n",
+        "    try:\n",
+        "        area_df = pd.read_csv(f'./Crop_NonCrop_Area_Sum_Export-Kenya-{cropmap}.csv')\n",
+        "    except:\n",
+        "        continue\n",
+        "    crop_area = int(area_df['crop_sum'][0])\n",
+        "    noncrop_area = int(area_df['noncrop_sum'][0])\n",
+        "    a_j[cropmap] = np.array([noncrop_area, crop_area])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3e853cd0",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# update a_j values with exported values\n",
+        "for cropmap in a_j.keys():\n",
+        "    try:\n",
+        "        area_df = pd.read_csv(f'./Crop_NonCrop_Area_Sum_Export-Kenya-{cropmap}.csv')\n",
+        "    except:\n",
+        "        continue\n",
+        "    crop_area = int(area_df['crop_sum'][0])\n",
+        "    noncrop_area = int(area_df['noncrop_sum'][0])\n",
+        "    a_j[cropmap] = np.array([noncrop_area, crop_area])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "zyR4qCJ49Rh5",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "zyR4qCJ49Rh5",
+        "outputId": "687d7607-e723-4097-9d70-a3b65e149b26"
+      },
+      "outputs": [],
+      "source": [
+        "# Change None to nan\n",
+        "a_j = {k: np.array([np.nan, np.nan]) if np.any(v == None) else v for k,v in a_j.items()}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "LY6Q_QtUgME_",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "LY6Q_QtUgME_",
+        "outputId": "38c43d9d-f9a4-4784-ad57-70c9bfc220c4"
+      },
+      "outputs": [],
+      "source": [
+        "from src.area_utils import compute_area_estimate, compute_area_error_matrix, compute_std_p_i\n",
+        "from sklearn.metrics import confusion_matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "oojPqwSboiWU",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "oojPqwSboiWU",
+        "outputId": "bf0c96a8-477b-4e7b-9e3c-e675aba506b8"
+      },
+      "outputs": [],
+      "source": [
+        "# compute area estimate for each map\n",
+        "def compute_area_estimate(dataset, true, pred, a_j, resolution):\n",
+        "    cm = confusion_matrix(true, pred)\n",
+        "    total_px = a_j.sum()\n",
+        "    w_j = a_j / total_px\n",
+        "\n",
+        "    am = compute_area_error_matrix(cm, w_j)\n",
+        "    a_i = am.sum(axis=1)\n",
+        "    std_a_i = compute_std_p_i(w_j, am, cm)\n",
+        "    err_a_i = 1.96 * std_a_i\n",
+        "\n",
+        "    a_px = total_px * a_i\n",
+        "    err_px = err_a_i * total_px\n",
+        "    return pd.DataFrame(\n",
+        "        data={\n",
+        "            \"dataset\": dataset,\n",
+        "            \"area_ha\": a_px[1] * (resolution**2) / (100**2),\n",
+        "            \"err_ha\": err_px[1] * (resolution**2) / (100**2),\n",
+        "        },\n",
+        "        index=[0],\n",
+        "    ).round(2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ti5ZXmbyn6Mm",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 196
+        },
+        "id": "ti5ZXmbyn6Mm",
+        "outputId": "a73ecb9c-d41e-4968-e987-041cab46daea"
+      },
+      "outputs": [],
+      "source": [
+        "comparisons = []\n",
+        "area_est = []\n",
+        "for cropmap in TARGETS.values():\n",
+        "    cropmap, resolution = cropmap.title, cropmap.resolution\n",
+        "    if cropmap not in gdf.columns:\n",
+        "        continue\n",
+        "    temp = gdf[[CLASS_COL, cropmap]].dropna()\n",
+        "    area = compute_area_estimate(cropmap, temp[CLASS_COL], temp[cropmap], a_j[cropmap], resolution)\n",
+        "    comparison = generate_report(cropmap, country, temp[CLASS_COL], temp[cropmap], a_j[cropmap], area_weighted=True)\n",
+        "    comparisons.append(comparison)\n",
+        "    area_est.append(area)\n",
+        "\n",
+        "comparisons = pd.concat(comparisons).set_index(['dataset'])\n",
+        "area_est = pd.concat(area_est).set_index(['dataset'])\n",
+        "\n",
+        "results = comparisons.merge(area_est, on='dataset')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "QrAgv7pP1lcz",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "QrAgv7pP1lcz",
+        "outputId": "6f33c955-6ceb-4295-84ed-4aaf65c1512f"
+      },
+      "outputs": [],
+      "source": [
+        "results.to_csv('results.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "nAj0p7VS1_2K",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "nAj0p7VS1_2K",
+        "outputId": "78579510-3b2c-4a4e-b9e0-97cb2f9837bc"
+      },
+      "outputs": [],
+      "source": [
+        "results[['crop_f1','accuracy','std_acc','crop_recall_pa','std_crop_pa','crop_precision_ua','std_crop_ua','area_ha','err_ha']]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fa969373",
+      "metadata": {
+        "id": "fa969373"
+      },
+      "source": [
+        "## 4. Plot area estimate and error"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fraQjcTMpTwp",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 540
+        },
+        "id": "fraQjcTMpTwp",
+        "outputId": "0b4817bc-04b7-45ea-a267-0777448edc38"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "fig, ax = plt.subplots()\n",
+        "\n",
+        "n = len(results)\n",
+        "colors = plt.cm.viridis(np.linspace(0, 1, n))\n",
+        "\n",
+        "ax.barh(\n",
+        "    results.index,\n",
+        "    results[\"area_ha\"],\n",
+        "    xerr=results[\"err_ha\"],\n",
+        "    align=\"center\",\n",
+        "    alpha=0.5,\n",
+        "    ecolor=\"black\",\n",
+        "    capsize=10,\n",
+        "    color=colors,\n",
+        ")\n",
+        "\n",
+        "for i, (value, err) in enumerate(zip(results[\"area_ha\"], results[\"err_ha\"])):\n",
+        "    ax.text(value, i, f\"{value} ± {err}\", ha=\"center\", va=\"bottom\")\n",
+        "\n",
+        "ax.set_ylabel(\"Area (ha)\")\n",
+        "ax.set_title(\"Area of cropland\")\n",
+        "ax.spines[\"right\"].set_visible(False)\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3.9.12 ('base')",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.12"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/src/compare_covermaps.py b/src/compare_covermaps.py
index d51a51f3..00ec229b 100644
--- a/src/compare_covermaps.py
+++ b/src/compare_covermaps.py
@@ -106,6 +106,8 @@ def __init__(
         title: str,
         ee_asset_str: str,
         resolution: int,
+        year=None,
+        collection_years=None,
         countries=None,
         probability=None,
         crop_labels=None,
@@ -115,6 +117,8 @@ def __init__(
         self.ee_asset_str = ee_asset_str
         self.ee_asset = eval(ee_asset_str.replace("\n", "").replace(" ", ""))
         self.resolution = resolution
+        self.year = year
+        self.collection_years = collection_years
 
         assert (probability is None) ^ (
             crop_labels is None
@@ -755,18 +759,21 @@ def generate_report(
         .filterDate("2019-01-01", "2019-12-31")""",
         resolution=100,
         crop_labels=[40],
+        year=2019,
     ),
     "worldcover-v100": Covermap(
         "worldcover-v100",
         'ee.ImageCollection("ESA/WorldCover/v100")',
         resolution=10,
         crop_labels=[40],
+        year=2020,
     ),
     "worldcover-v200": Covermap(
         "worldcover-v200",
         'ee.ImageCollection("ESA/WorldCover/v200")',
         resolution=10,
         crop_labels=[40],
+        year=2021,
     ),
     "worldcereal-v100": Covermap(
         "worldcereal-v100",
@@ -778,12 +785,14 @@ def generate_report(
         )""",
         resolution=10,
         crop_labels=[100],
+        year=2021,
     ),
     "glad": Covermap(
         "glad",
         'ee.ImageCollection("users/potapovpeter/Global_cropland_2019")',
         resolution=30,
         probability=0.5,
+        year=2019,
     ),
     # "gfsad": Covermap(
     #     "gfsad",
@@ -797,6 +806,7 @@ def generate_report(
         resolution=1000,
         probability=100,
         countries=[country for country in TEST_COUNTRIES.keys() if country != "Hawaii"],
+        year=2017,
     ),
     "dynamicworld": Covermap(
         "dynamicworld",
@@ -808,18 +818,21 @@ def generate_report(
         )""",
         resolution=10,
         crop_labels=[4],
+        year=2019,
     ),
     "gfsad-gcep": Covermap(
         "gfsad-gcep",
         'ee.ImageCollection("projects/sat-io/open-datasets/GFSAD/GCEP30")',
         resolution=30,
         crop_labels=[2],
+        year=2015,
     ),
     "gfsad-lgrip": Covermap(
         "gfsad-lgrip",
         'ee.ImageCollection("projects/sat-io/open-datasets/GFSAD/LGRIP30")',
         resolution=30,
         crop_labels=[2, 3],
+        year=2015,
     ),
     "digital-earth-africa": Covermap(
         "digital-earth-africa",
@@ -831,6 +844,7 @@ def generate_report(
         resolution=10,
         crop_labels=[1],
         countries=[country for country in TEST_COUNTRIES.keys() if country != "Hawaii"],
+        year=2019,
     ),
     "esa-cci-africa": Covermap(
         "esa-cci-africa",
@@ -840,6 +854,7 @@ def generate_report(
         resolution=20,
         crop_labels=[4],
         countries=[country for country in TEST_COUNTRIES.keys() if country != "Hawaii"],
+        year=2016,
     ),
     "globcover-v23": Covermap(
         "globcover-v23",
@@ -848,6 +863,7 @@ def generate_report(
         )""",
         resolution=300,
         crop_labels=[11, 14, 20, 30],
+        year=2009,
     ),
     "globcover-v22": Covermap(
         "globcover-v22",
@@ -856,6 +872,7 @@ def generate_report(
         )""",
         resolution=300,
         crop_labels=[11, 14, 20, 30],
+        year=2005,
     ),
     "esri-lulc": Covermap(
         "esri-lulc",
@@ -864,6 +881,7 @@ def generate_report(
         ).filter(ee.Filter.date("2019-01-01", "2020-01-01"))""",
         resolution=10,
         crop_labels=[5],
+        year=2019,
     ),
     "nabil-etal-2021": Covermap(
         "nabil-etal-2021",
@@ -873,6 +891,7 @@ def generate_report(
         resolution=30,
         crop_labels=[2],
         countries=[country for country in TEST_COUNTRIES.keys() if country != "Hawaii"],
+        year=2017,
     ),
     "harvest-crop-maps": Covermap(
         "harvest-crop-maps",
@@ -880,6 +899,7 @@ def generate_report(
         resolution=10,
         probability=0.5,
         countries=["Togo", "Kenya", "Malawi"],
+        collection_years=[2019, 2019, 2020],
     ),
     "harvest-dev": Covermap(
         "harvest-dev",
@@ -925,5 +945,20 @@ def generate_report(
             "Tigray2020",
             "Rwanda",
         ],
+        collection_years=[
+            2019,
+            2020,
+            2019,
+            2019,
+            2019,
+            2020,
+            2019,
+            2019,
+            2019,
+            2020,
+            2021,
+            2020,
+            2019,
+        ],
     ),
 }