What problem does it solve? Working with geospatial data requires navigating dozens of libraries, coordinate systems, satellite data sources, and analysis techniques. This Skill consolidates remote sensing, GIS operations, spatial statistics, and Earth observation machine learning into one reference with 500+ runnable code examples, eliminating the need to search across fragmented documentation. ## Core Features & Use Cases - Remote Sensing Workflows: Process Sentinel, Landsat, MODIS, SAR, and hyperspectral imagery, compute spectral indices like NDVI/EVI/NDWI, and run cloud masking and classification pipelines. - Vector and Raster Analysis: Perform spatial joins, overlays, buffering, terrain analysis, network routing, and coordinate transformations with GeoPandas, Rasterio, GDAL, and Shapely. - Cloud-Native and Big Data Processing: Query STAC catalogs, stream Cloud-Optimized GeoTIFFs, use Google Earth Engine and Planetary Computer, and scale with Dask, GPU acceleration, and Zarr. - Use Case: A researcher needs to map flood risk for a region. They use this Skill to download Sentinel-2 imagery via STAC, calculate NDWI, delineate watersheds from a DEM, and overlay exposed settlements to produce a risk map. ## Quick Start Ask the AI to calculate NDVI from a Sentinel-2 image and export the result as a Cloud-Optimized GeoTIFF.
