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<!DOCTYPE html>
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<title>GeoKG & GeoAI Workshop 2021</title>
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<a class="nav-link js-scroll-trigger" href="#cfp">Call For Paper</a>
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<a class="nav-link js-scroll-trigger" href="#list">List of Topics</a>
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<a class="nav-link js-scroll-trigger" href="#date">Important Dates</a>
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<a class="nav-link js-scroll-trigger" href="#info">Info for Author</a>
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<div class="container text-center" style="background-color: white;opacity: 0.85;color:black">
<h2>The 1st International Workshop on</h2>
<h1>Methods, Models, and Resources for Geospatial Knowledge Graphs and GeoAI</h1>
<h2>co-located with GIScience 2021, Poznań, Poland</h2>
</div>
</header>
<!-- <section id="covid-19">
<div class="container ">
<div class="col-lg-10 mx-auto covid-19-announcement" style="color: red;font-weight: bold;">
<p>
Workshop Date Update: </br>
We pay close attention to the current COVID-19 situation. We will keep our original schedule. If it turns out that we cannot do personal meetings, we will organize it virtually. These important dates can be also extended based on the situation.
Due to the uncertain impacts of COVID-19 in next months, the organizing committee has decided to postpone the workshop (GeoKG & GeoAI 2021) in conjunction with the GIScience conference until Fall 2021.
</p>
</div>
</div>
</section> -->
<section id="program" >
<div class="container" >
<div class="row">
<div class="col-lg-10 mx-auto">
<h2>Workshop Program</h2>
<p> This workshop will be held on Sept. 27, 2021 (CEST).</p>
<table>
<tr>
<th style="width:20%">Session</th>
<th style="width:15%">Time</th>
<th style="width:30%">Speaker</th>
<th style="width:35%">Title</th>
</tr>
<tr>
<td>Opening Introduction</td>
<td>15:30 - 16:00</td>
<td>Gengchen Mai, Ling Cai</td>
<td>Geospatial Knowledge Graph and Spatially-Explicit AI</td>
</tr>
<tr>
<td>Keynote</td>
<td>16:00 - 17:00</td>
<td>Pascal Hitzler</td>
<td>The KnowWhereGraph</td>
</tr>
<tr>
<td>Break</td>
<td>17:00 - 17:10</td>
<td> </td>
<td> </td>
</tr>
<tr>
<th rowspan="3">Session 1: Geospatial Semantics and GeoKG</th>
<td>17:10 - 17:30</td>
<td>Shirly Stephen, Wenwen Li and Torsten Hahmann</td>
<td>Geo-Situation for Modeling Causality of Geo-Events in Knowledge Graphs</td>
</tr>
<tr>
<td>17:30 - 17:50</td>
<td>Marvin Mc Cutchan and Ioannis Giannopoulos</td>
<td>Geospatial Semantics and Geographic Aware ANN</td>
</tr>
<tr>
<td>17:50 - 18:10</td>
<td>Yuanyuan Tian and Wenwen Li</td>
<td>GeoAI for Knowledge Graph Construction: Identifying Causality Between Cascading Events to Support Environmental Resilience Research</td>
</tr>
<tr>
<td>Break</td>
<td>18:10 - 18:20</td>
<td> </td>
<td> </td>
</tr>
<tr>
<th rowspan="4">Session 2: GeoAI</th>
<td>18:20 - 18:40</td>
<td>Peng Yue, Boyi Shangguan, Lei Hu, Chenxiao Zhang, Liangcun Jiang and Zhe Fang</td>
<td>Quality Considerations for AI Training Data in Remote Sensing</td>
</tr>
<tr>
<td>18:40 - 19:00</td>
<td>Jin Xing and Renee Sieber</td>
<td>Challenges of Using XAI for Geographic Data Analytics</td>
</tr>
<tr>
<td>19:00 - 19:20</td>
<td>Cláudia Rodrigues, Ana Alves, Marco Veloso and Carlos Bento</td>
<td>Identification of the User’s Geographic Map</td>
</tr>
<tr>
<td>19:20 - 19:40</td>
<td>Haojian Liang and Shaohua Wang</td>
<td>A New Approach Based on Graph Neural Network for Solving p-center Problems</td>
</tr>
<tr>
<td>Discussion & Closing Remark</td>
<td>19:40 - 20:00</td>
<td>PC Chair</td>
<td> </td>
</tr>
</table>
</div>
</div>
</div>
</section>
<section id="cfp" >
<div class="container" >
<div class="row">
<div class="col-lg-10 mx-auto">
<h2>Call For Paper</h2>
<p>The rapid increase in high-quality data, advanced machine learning algorithms, and the availability of fast hardware have largely contributed to a renewed interest in Artificial Intelligence (AI). Despite many successful stories in computer vision, natural language processing, and speech recognition, there are many challenges that remain to be solved, such as large scale neural symbolic reasoning based on unstructured text and automatic knowledge graph construction. Interestingly, nowadays, one of the most prominent topics in AI is the combination of representation learning techniques (Connectionist Artificial Intelligence) with symbolic representation and reasoning associated with knowledge graphs (Symbolic Artificial Intelligence), in order to develop scalable and interpretable machine learning models. One good example is knowledge graph embedding models that aim at representing components of knowledge graphs, such as entities and relations, as continuous vectors or matrices while preserving the graph’s structural information. From a geospatial point-of-view, GeoAI, as an interdisciplinary field of GIScience and AI, advocates the idea of developing and utilizing AI techniques in geography and earth science. Geospatial knowledge graphs, as symbolic representations of geospatial knowledge, go to the core of GeoAI and facilitate many intelligent applications such as geospatial data integration and knowledge discovery. In fact, geospatial data plays an important role in the Linked Open Data cloud, an open-sourced cross-domain knowledge graph, since spatio-temporal scopes are essential for describing events, people, and objects. However, many relational machine learning models treat geographic entities as ordinary entities in which the spatial footprints of places are neglected and the distance decay effect is ignored. This results in suboptimal performance in many geospatial related tasks such as geospatial knowledge graph completion, geographic question answering, geographic entity alignment, as well as geographic knowledge graph summarization. </p>
<!-- <br></br> -->
<p>In addition, there exist many demands for further advancements in other research topics related to GeoAI, such as remote sensing and street view image analysis, transportation modeling, and geo-text analysis. There are, for instance, many challenges in the adaptation of deep learning techniques to these scenarios, including the limited availability of labeled data or the difficulty of the models to generalize between locations. Incorporating geo-spatial knowledge (i.e., prior knowledge about the structure of objects on the surface of the Earth, and about the fundamental rules of geography) directly into deep neural network models, in the form of specially designed components and/or regularization schemes, is a promising approach to address the aforementioned challenges.</p>
<!-- <br></br> -->
<p>
Based on the above observations, this combined workshop and tutorial emphasizes the importance of geospatial information and principles in designing, developing, and utilizing geospatial knowledge graphs and other GeoAI techniques. Accordingly, we call for new methods, models, and resources for advancing research related to Geospatial Knowledge Graphs and GeoAI.
</p>
<!-- <br></br> -->
</div>
</div>
</div>
</section>
<section id="list" class="bg-light">
<div class="container">
<div class="row">
<div class="col-lg-10 mx-auto">
<h2>List of Relevant Topics</h2>
<ul>
<li><strong>Deep Learning and Reinforcement Learning on Geospatial Knowledge Graphs</strong></li>
<ul>
<li>Geographic Knowledge Graph Embeddings</li>
<li>Geographic Question Answering and Semantic Parsing based on Knowledge Graphs</li>
<li>Geospatial Knowledge Graph Summarization</li>
</ul>
<li><strong>Geo-Ontology Engineering and Geospatial Knowledge Graph Construction</strong></li>
<ul>
<li>Spatio-Temporal Scoping of Knowledge Graphs</li>
<li>Gazetteer Data Management</li>
<li>Coreference Resolution for Geographic Entities</li>
<li>Geographic Ontology Alignment</li>
<li>Geospatial Knowledge Graph Construction and Completion</li>
<li>Geographic Entity Similarity Measurement</li>
</ul>
<li><strong>Querying and Visualization on Geospatial Knowledge Graphs</strong></li>
<ul>
<li>GeoSPARQL and Spatial Query Evaluation</li>
<li>Knowledge Graph Visualization</li>
<li>Geo-Ontology Visualization</li>
</ul>
<li><strong>Geographic Information Retrieval and Geo-Text Analysis</strong></li>
<ul>
<li>Text Geoparsing, Toponym Recognition, and Toponym Resolution</li>
<li>Information Extraction from Location-Based Social Media </li>
<li>Searching and Indexing Texts based on Locations</li>
<li>Open Domain Geographic Question Answering</li>
<li>Human Experience Extraction from Place Descriptions</li>
</ul>
<li><strong>Spatially Explicit Machine Learning Methods and Models for GeoAI</strong></li>
<ul>
<li>Bridging GIScience Methods with Deep Learning</li>
<li>Model Invariance/Equivariance for Geospatial Applications (e.g., equivariance to changes in input scale or rotation)</li>
</ul>
<li><strong>GeoAI for Geospatial Image Analysis</strong></li>
<ul>
<li>Classification, Segmentation, and Object/Instance Recognition</li>
<li>Remote Sensing Images</li>
<li>Street View Images</li>
<li>Scanned Paper Maps and Historical Imagery</li>
</ul>
<li><strong>GeoAI Resources and Infrastructures</strong></li>
<ul>
<li>Data Augmentation Strategies and Dataset Generation</li>
<li>Development of benchmark Datasets, Tools, and Platforms</li></li>
<li>Spatial Data Infrastructures Supporting GeoAI</li>
</ul>
<li><strong>Other GeoAI Topics and Applications</strong></li>
<ul>
<li>Transportation Modeling and Trajectory Data Analysis</li>
<li>Spatial Optimization</li>
<li>Spatio-Temporal Data Fusion and Assimilation</li>
<li>Spatial Simulation (i.e. Learning Agents in Agent-based Simulations)</li>
</ul>
</ul>
</div>
</div>
</div>
</section>
<section id="date">
<div class="container">
<div class="row">
<div class="col-lg-10 mx-auto">
<h2>Important Dates</h2>
<ul>
<li><strong>Paper submission:</strong> July 31, 2021 (Extended)</li>
<li><strong>Acceptance decision:</strong> August 31, 2021</li>
<li><strong>Camera ready version:</strong> Sepember 10, 2021</li>
<li><strong>Workshop date:</strong> September 27, 2021</li>
</ul>
</ul>
</div>
</div>
</div>
</section>
<!-- <section id="format" class="bg-light">
<div class="container">
<div class="row">
<div class="col-lg-10 mx-auto">
<h2>Workshop Format</h2>
<p>This workshop will have a half-day for tutorial sessions and a half-day for research presentations. We welcome short research articles and industry demonstration papers regarding relevant topics. Typically, the page limit is 4 pages and the recommended template is the 2019 template provided by LIPIcs <a href="http://drops.dagstuhl.de/styles/lipics-v2019/lipics-v2019-authors.tgz" >(http://drops.dagstuhl.de/styles/lipics-v2019/lipics-v2019-authors.tgz)</a>. The submission Web page for both tracks of GIScience 2021 is: <a href="https://easychair.org/conferences/?conf=geokg2021"> https://easychair.org/conferences/?conf=geokg2021.</a></p>
</div>
</div>
</div>
</section> -->
<section id="info" class="bg-light">
<div class="container">
<div class="row">
<div class="col-lg-10 mx-auto">
<h2>Information For Authors</h2>
<p>This workshop will have a half-day for tutorial sessions and a half-day for research presentations. We welcome short research articles and industry demonstration papers regarding relevant topics. The page limit is 4 pages and the recommended template is the 2019 template provided by LIPIcs <a href="http://drops.dagstuhl.de/styles/lipics-v2019/lipics-v2019-authors.tgz" target="_blank">(http://drops.dagstuhl.de/styles/lipics-v2019/lipics-v2019-authors.tgz)</a>. The submission Web page for both tracks of GIScience 2021 is: <a href="https://easychair.org/conferences/?conf=geokg21" target="_blank"> https://easychair.org/conferences/?conf=geokg21.</a></p>
</div>
</div>
<div class="row">
<div class="col-lg-10 mx-auto">
<h2>Journal Special Issue</h2>
<p>A special issue, with the same scope as this workshop, will be published in <a href="https://onlinelibrary.wiley.com/journal/14679671" target="_blank"> Transactions in GIS (TGIS)</a>. Submissions to the special issue may be made by either the workshop participants or others interested in the theme. Participation in the workshop does not guarantee acceptance in the special feature, and all submissions will be submitted to a review process that follows TGIS standards. However, <b>we strongly recommend researchers first submit their initial work to this workshop as a preselection step</b>. </p>
<p>The full papers will be due on Feb 15, 2022, thus allowing to incorporate feedback and new insights gained at the workshop, and potentially even forming new teams of authors. Please refer to the official CFP of this special issue from <a href="https://onlinelibrary.wiley.com/page/journal/14679671/homepage/featured-collections" target="_blank">TGIS webpage</a> and <a href="https://onlinelibrary.wiley.com/pb-assets/14679671/TGIS%20GeoKG%202021%20special%20issue%20CFP-1626979962320.pdf" target="_blank">this PDF</a>.</p>
</div>
</div>
</div>
</section>
<!-- <section id="organizer" class="bg-light">
<div class="container">
<div class="row">
<div class="col-lg-10 mx-auto">
<h2>Organizers</h2>
<ul>
<li><a href="http://www.geog.ucsb.edu/~gengchen_mai/" target="_blank">Gengchen Mai</a>, University of California, Santa Barbara</li>
<li><a href="http://www.acsu.buffalo.edu/~yhu42/" target="_blank">Yingjie Hu</a>, University at Buffalo</li>
<li><a href="https://geography.wisc.edu/profile.php?p=1197" target="_blank">Song Gao</a>, University of Wisconsin-Madison</li>
<li><a href="https://stko.geog.ucsb.edu/people" target="_blank">Ling Cai</a>, University of California, Santa Barbara</li>
<li><a href="http://web.ist.utl.pt/bruno.g.martins/" target="_blank">Bruno Martins</a>, University of Lisbon</li>
<li><a href="http://www.johannesscholz.net/" target="_blank">Johannes Scholz</a>, Graz University of Technology</li>
<li><a href="https://www.udel.edu/academics/colleges/ceoe/departments/gss/faculty/jing-gao/" target="_blank">Jing Gao</a>, University of Delaware</li>
</ul>
</div>
</div>
</div>
</section> -->
<section id="PC_member">
<div class="container">
<div class="row">
<div class="col-lg-10 mx-auto">
<h2>Organizers</h2>
<ul>
<li><a href="https://gengchenmai.github.io/" target="_blank">Gengchen Mai</a>, Stanford AI Lab, Stanford University</li>
<li><a href="http://www.acsu.buffalo.edu/~yhu42/" target="_blank">Yingjie Hu</a>, Department of Geography, University at Buffalo</li>
<li><a href="https://geography.wisc.edu/profile.php?p=1197" target="_blank">Song Gao</a>, Department of Geography, University of Wisconsin-Madison</li>
<li><a href="https://stko.geog.ucsb.edu/people" target="_blank">Ling Cai</a>, Department of Geography, University of California, Santa Barbara</li>
<li><a href="http://web.ist.utl.pt/bruno.g.martins/" target="_blank">Bruno Martins</a>, Instituto Superior Técnico, University of Lisbon</li>
<li><a href="http://www.johannesscholz.net/" target="_blank">Johannes Scholz</a>, Institute of Geodesy, Graz University of Technology</li>
<li><a href="https://www.udel.edu/academics/colleges/ceoe/departments/gss/faculty/jing-gao/" target="_blank">Jing Gao</a>, Department of Geography and Spatial Sciences, University of Delaware</li>
</ul>
</div>
</div>
<div class="row">
<div class="col-lg-10 mx-auto">
<h2>PC member</h2>
<ul>
<li><a href="https://www.lunduniversity.lu.se/lucat/user/71077542e8e3492940fd86b554904d67" target="_blank">Weiming Huang</a>, GIS Centre, Lund University</li>
<li><a href="https://www.linkedin.com/in/fei-du/" target="_blank"> Fei Du</a>, Apple Map</li>
<li><a href="https://daselab.cs.ksu.edu/people/cogan-shimizu" target="_blank">Cogan Shimizu</a>, Kansas State University</li>
<li><a href="http://www.geog.ucsb.edu/~boyan/" target="_blank">Bo Yan</a>, Google </li>
<li><a href="https://noon99jaki.github.io/" target="_blank">Ni Lao</a>, SayMosaic</li>
<li><a href="http://xiliu.city/" target="_blank">Xi Liu</a>, Google Inc.</li>
<li><a href="https://spatial.usc.edu/team-view/yao-yi-chiang/" target="_blank">Yao-Yi Chiang</a>, University of Southern California</li>
<li><a href="http://www.geog.ucsb.edu/~zhu/" target="_blank">Rui Zhu</a>, University of California, Santa Barbara</li>
<li><a href="https://sgsup.asu.edu/wenwen-li" target="_blank">Wenwen Li</a>, Arizona State University</li>
<!-- <li><a href="https://www.uu.nl/staff/sscheider" target="_blank">Simon Scheider</a>, University Utrecht, Department of Human Geography and Spatial Planning</li> -->
<li><a href="https://vbn.aau.dk/en/persons/137743" target="_blank">Carsten Keßler</a>, Department of Planning, Aalborg University Copenhagen</li>
<li><a href="https://www.usgs.gov/staff-profiles/dalia-e-varanka?qt-staff_profile_science_products=3#qt-staff_profile_science_products" target="_blank">Dalia Varanka</a>, U.S. Geological Survey</li>
<li><a href="http://www.raubal.ethz.ch/" target="_blank">Martin Raubal</a>, ETH Zurich</li>
<!-- <li><a href="https://blake-regalia.net/" target="_blank">Blake Regalia</a>, NASA JPL</li> -->
<li><a href="https://www.colorado.edu/geography/morteza-karimzadeh" target="_blank">Morteza Karimzadeh</a>, University of Colorado Boulder</li>
<li><a href="https://www.linkedin.com/in/fan-zhang-71511468/?originalSubdomain=hk" target="_blank">Fan Zhang</a>, MIT</li>
<li><a href="https://www.linkedin.com/in/hongxu-ma-7458a735/" target="_blank">Hongxu Ma</a>, Google X</li>
<li><a href="https://scholars.library.tamu.edu/vivo/display/n96eca29a/Persons/View%20All" target="_blank">Xinyue Ye</a>, Texas A&M University</li>
<li><a href="https://geography.wisc.edu/profile.php?p=1218" target="_blank">Yuhao Kang</a>, University of Wisconsin-Madison</li>
<!-- <li>...</li> -->
</ul>
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<h2>List of Relevant Papers</h2>
<ul>
<p class="itemDetails"><span class="paperIndex">1. </span><b>Gengchen Mai</b>, Krzysztof Janowicz, <b>Ling Cai</b>, Rui Zhu, Blake Regalia, Bo Yan, Meilin Shi, Ni Lao. (2020) <a target='_blank' href="papers/2020-TGIS-SE-KGE.pdf">SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting</a>. <i>Transactions in GIS</i>. <a target='_blank' href="">DOI:10.1111/TGIS.12629</a> <a target='_blank' href="">[arxiv paper]</a> </p>
<p class="itemDetails"><span class="paperIndex">2. </span><b>Gengchen Mai</b>, Krzysztof Janowicz, Bo Yan, Rui Zhu, <b>Ling Cai</b>, Ni Lao. <a target='_blank' href="papers/2020-ICLR2020-Space2Vec.pdf">Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells</a>, In: <i>Proceedings of ICLR 2020</i>, Apr. 26 - 30, 2020, Addis Ababa, ETHIOPIA . <a target='_blank' href="https://openreview.net/forum?id=rJljdh4KDH">[OpenReview paper]</a> <a target='_blank' href="https://arxiv.org/abs/2003.00824">[arxiv paper]</a> <a target='_blank' href="https://github.com/gengchenmai/space2vec">[code]</a> <a target='_blank' href="https://www.youtube.com/watch?v=7hTtBXqea1E">[video]</a> <a target='_blank' href="presentations/2020-ICLR2020-Space2Vec.pdf">[slides]</a> </a></p>
<p class="itemDetails"><span class="paperIndex">3. </span><b>Gengchen Mai</b>, Krzysztof Janowicz, Bo Yan, Simon Scheider. <a target="_blank" href="papers/2019-TGIS-LDConnector.pdf">Deeply Integrating Linked Data with Geographic Information Systems</a>. <i>Transactions in GIS</i>, 23(2019), 579-600. <a target="_blank" href="https://onlinelibrary.wiley.com/doi/full/10.1111/tgis.12538">DOI:10.1111/tgis.12538</a> <a href="https://github.com/gengchenmai/esri-linked-data-connector" target="_blank">[code]</a></p>
<p class="itemDetails"><span class="paperIndex">4. </span><b>Gengchen Mai</b>, Bo Yan, Krzysztof Janowicz, Rui Zhu. <a target="_blank" href="papers/2019-AGILE19_TransGeo.pdf">Relaxing Unanswerable Geographic Questions Using A Spatially Explicit Knowledge Graph Embedding Model</a>, In: <i>Proceedings of AGILE 2019</i>, June 17 - 20, 2019, Limassol, Cyprus. </p>
<p class="itemDetails"><span class="paperIndex">5. </span><b>Yingjie Hu</b>, Chengbin Deng, Zhou Zhou. (2019) <a href="https://www.tandfonline.com/eprint/NKs3TjDt5FCrKDsgSTrD/full?target=10.1080/24694452.2018.1535886">A semantic and sentiment analysis on online neighborhood reviews for understanding the perceptions of people toward their living environment</a>. <i>Annals of the American Association of Geographers</i>, 109(4), 1052-1073.</p>
<p class="itemDetails"><span class="paperIndex">6. </span><b>Yingjie Hu</b>, <b>Song Gao</b>, Dalton Lunga, Wenwen Li, Shawn Newsam & Budhendra Bhaduri. (2019) <a target='_blank' href="https://dl.acm.org/doi/abs/10.1145/3377000.3377002">GeoAI at ACM SIGSPATIAL: progress, challenges, and future directions</a>. <i>SIGSPATIAL Special</i>, 11(2), 5-15.</p>
<p class="itemDetails"><span class="paperIndex">7. </span>Jimin Wang, <b>Yingjie Hu</b>, Kenneth Joseph. (2020) <a target='_blank' href="https://www.acsu.buffalo.edu/~yhu42/papers/2020_TGIS_NeuroTPR.pdf">NeuroTPR: A Neuro-net ToPonym Recognition model for extracting locations from social media messages</a>. <i>Transactions in GIS</i>, in press</p>
<p class="itemDetails"><span class="paperIndex">8. </span>Jimin Wang, <b>Yingjie Hu</b> (2019): <a target="_blank" href="https://www.acsu.buffalo.edu/~yhu42/papers/2019_TGIS_EUPEG.pdf">Enhancing spatial and textual analysis with EUPEG: an extensible and unified platform for evaluating geoparsers</a>. <i>Transactions in GIS</i>, 23(6), 1393-1419.</p>
<p class="itemDetails"><span class="paperIndex">9. </span>Yuhao Kang, <b>Song Gao</b>, Robert E. Roth. (2019) <a target="_blank" href="https://www.tandfonline.com/doi/abs/10.1080/23729333.2019.1615729?journalCode=tica20">Transferring Multiscale Map Styles Using Generative Adversarial Networks</a>. International Journal of Cartography. 5, 115-141.</p>
<p class="itemDetails"><span class="paperIndex">10. </span><b>Ling Cai</b>, Krzysztof Janowicz, <b>Gengchen Mai</b>, Bo Yan, Rui Zhu. (2020) <a target="_blank" href="">Traffic Transformer: Capturing the Continuity and Periodicity of Time Series for Traffic Forecasting</a>. <i>Transactions in GIS</i>. in press </p>
<p class="itemDetails"><span class="paperIndex">11. </span>Bo Yan, Krzysztof Janowicz, <b>Gengchen Mai</b>, Rui Zhu. <a target='_blank' href="papers/2019-TGIS-GKG_RL.pdf">A Spatially-Explicit Reinforcement Learning Model for Geographic Knowledge Graph Summarization</a>. <i>Transactions in GIS</i>, 23(2019), 620-640. <a target='_blank' href="https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12547">DOI:10.1111/tgis.12547</a></p>
<p class="itemDetails"><span class="paperIndex">12. </span>Bo Yan, Krzysztof Janowicz, <b>Gengchen Mai</b>, <b>Song Gao</b>. <a target='_blank' href="papers/2017-ACM_SIGSPATIAL17_place2vec.pdf">From ITDL to Place2Vec -- Reasoning About Place Type Similarity and Relatedness by Learning Embeddings From Augmented Spatial Contexts</a>, In: <i>Proceedings of the 25th International Conference on Advances in Ge-ographic Information Systems (ACM SIGSPATIAL 2017)</i>, November 7 - 10, 2017, Redondo Beach, California, USA.</p>
<p class="itemDetails"><span class="paperIndex">13. </span>Krzysztof Janowicz, <b>Song Gao</b>, Grant McKenzie, <b>Yingjie Hu</b>, and Budhendra Bhaduri. <a target='_blank' href="https://www.tandfonline.com/doi/full/10.1080/13658816.2019.1684500">GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond.</a>, <i>International Journal of Geographical Information Science</i> 34(2020): 625-636.</p>
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<h2>Contact</h2>
<p>For any further information, please contact <a href="mailto:gengchen_mai@geog.ucsb.edu">Gengchen Mai</a> or <a href="mailto:lingcai@ucsb.edu ">Ling Cai</a>. </p>
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