πŸš€ Rastereasy

rastereasy is a Python library designed to provide a high-level, human-readable interface for common geospatial raster and vector operations (e.g., *.tif, *.jp2, *.shp). Built on well-established libraries including rasterio, numpy, shapely, geopandas, and scikit-learn, it enables users to perform typical GIS tasksβ€”such as resampling, cropping, reprojection, stacking, clipping rasters with shapefiles, or rasterizing vector layersβ€”in just a few lines of code. Some basic Machine Learning functionalities (clustering, fusion) are also implemented.

It aims to streamline geospatial workflows by providing intuitive tools for:

  • Reading and processing raster and vector files.

  • Resampling, cropping, reprojecting, filtering, stacking, and more.

  • Creating visualizations (e.g., color composites, interactive spectral plots).

  • Training and applying classical Machine Learning algorithms.

  • Performing late fusion of classifications (e.g., Dempster-Shafer theory).

  • Performing dimensionality reduction (PCA, LLE, t-SNE) on spectral bands.

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πŸ“š Getting Started

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πŸ› οΈ API Reference

API Reference

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πŸš€ Main Features

  • Easy reading and writing of georeferenced raster and vector data.

  • Intuitive tools for reprojection, resampling, cropping, and mosaicking.

  • Support for visualization and spectral analysis.

  • Integration with scikit-learn for machine learning tasks.

  • Tools for classification fusion and spatial reasoning.

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πŸ” Indices and Tables