geomaster

Compute NDVI from Sentinel-2 imagery by loading raster bands with rasterio and numpy, then applying the normalized difference vegetation index formula to write a CRS-aware NDVI GeoTIFF. GeoMaster provides repeatable steps for this remote sensing index generation.

Handle CRS-safe reprojection for vector and raster datasets using GeoPandas, Shapely, and PyProj to manage EPSG/UTM zones and distinguish projected versus geographic coordinate systems. GeoMaster enforces these best practices to prevent spatial joins and overlay misalignment.

You need to install core geospatial libraries GDAL, Rasterio, GeoPandas, Shapely, and PyProj to perform geospatial analysis with GeoMaster. These dependencies provide the foundational raster loading, vector manipulation, and coordinate transformation capabilities required for deterministic analysis steps.

You can use STAC and COG for cloud-native Earth observation processing to acquire satellite products like Sentinel, Landsat, and MODIS. GeoMaster supports these cloud IO patterns to streamline access for optical, SAR, and hyperspectral data workflows.

Run spatial machine learning on remote sensing data by implementing classification models like RF, SVM, CNN, U-Net, or GNN within GeoMaster. It integrates ML steps with cloud-native workflows to generate classification outputs and model features from processed raster data.

Spatial joins and overlays return misaligned geographic data when vector and raster layers lack matching coordinate reference systems. GeoMaster prevents this by validating CRS formats and enforcing projected versus geographic handling before executing spatial statistics and network analysis.