geomaster

You calculate NDVI from Sentinel-2 satellite imagery by running Python code that processes raster bands using rasterio and GDAL to output a normalized vegetation index raster.

Remote sensing and spatial analysis workflows require Python libraries like GDAL, rasterio, geopandas, shapely, and pyproj to execute vector and raster data processing operations.

Yes, you can apply machine learning models to classify land cover from raster data by leveraging integrated scikit-learn and torchgeo libraries for image classification and spatial prediction.

This geospatial workflow supports point cloud data processing with LAZ files by utilizing the laspy and pylas dependencies to parse and manipulate 3D spatial data outputs.

To handle large raster datasets for Earth observation without memory errors, use xarray and dask-geopandas to parallelize spatial analysis operations and process raster arrays efficiently.

To search and access satellite imagery from Microsoft Planetary Computer, use the pystac-client and planetary-computer Python libraries to query STAC catalogs and retrieve remote sensing data assets.