geomaster

To calculate NDVI from Sentinel-2 satellite imagery, you can use this Skill to process raster data, apply spectral index formulas, and generate vegetation density outputs for Earth observation analysis.

Yes, you can use machine learning for land cover classification with raster data by leveraging integrated libraries like scikit-learn, xgboost, and torch-geometric to train models on satellite imagery.

Yes, this supports spatial network analysis and routing with OpenStreetMap data by utilizing osmnx and networkx to extract vector graphs, compute shortest paths, and perform topological spatial statistics.

The best way to process and classify LiDAR point cloud data is using integrated libraries like laspy, open3d, and pdal to read 3D point structures, filter noise, and extract elevation features for spatial analysis.

You can query cloud-native geospatial datasets using STAC catalogs by utilizing pystac-client and planetary-computer APIs to search, connect, and retrieve satellite imagery metadata without downloading full scenes.

Yes, you can perform spatial database operations using PostGIS within an automated workflow to store vector geometries, execute spatial SQL queries, and manage large-scale geospatial relational outputs efficiently.