Using Sentinel-2 satellite imagery, in this project I demonstrate how to use machine learning clustering techniques to classify various land cover types. Using Python libraries and Sentinel-2 satellite imagery in Algezeria state, Sudan. Clustering segments satellite images into meaningful groups based on spectral information. This approach helps in analyzing environmental and land-use patterns and supports decision-making in agriculture, urban planning, and conservation.
- Utilized Sentinel-2 images for unsupervised clustering.
- Explored K-means clustering algorithm.
- Python libraries: rasterio, scikit-learn, numpy, matplotlib
- Read more: Towards Data Science Article.
