geomaster

To compute NDVI from a Sentinel-2 GeoTIFF, you provide the raster file to the Skill, which processes the spectral bands and writes the resulting NDVI values to a new GeoTIFF output.

CRS-safe spatial analysis requires correctly matching coordinate reference systems across vector and raster datasets; the Skill handles this by reprojecting inputs to a suitable CRS before performing spatial operations.

Yes, cloud-native satellite data retrieval via STAC and COG is supported, enabling you to access and process Planetary Computer-style Earth observation datasets directly within your geospatial analysis workflow.

Geospatial processing requires Python multi-library integration including GDAL, Rasterio, and GeoPandas for spatial operations, plus optional ML and cloud-native data access tooling for spatial ML training and inference.

Yes, Earth-observation analytics support terrain and hydrology modeling by processing remote sensing, GIS, and spatial datasets across vectors, rasters, networks, and point clouds.

Spatial ML training for land cover classification is supported by applying classical models and deep learning patterns to Earth observation imagery, enabling spatial prediction workflows.