Remote Sensing Python Packages

gdal

GDAL is an open source MIT licensed translator library for raster and vector geospatial data formats.

sahi

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

earthaccess

Python Library for NASA Earthdata APIs

geemap

A Python package for interactive geospatial analysis and visualization with Google Earth Engine.

torchgeo

TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data

whitebox

WhiteboxTools Python Frontend

whiteboxgui

An interactive GUI for WhiteboxTools in a Jupyter-based environment

pyriemann

Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python

openeo

Python client API for OpenEO

spectral

Python module for hyperspectral image processing

datacube

Open Data Cube analyses continental scale Earth Observation data through time

ee-extra

A ninja python package that unifies the Google Earth Engine ecosystem.

geetools

A collection of tools to work with Google Earth Engine Python API

pygaul

Easy access to administrative boundary defined by FAO GAUL from Python scripts

eodag-cube

Xarray data access for EODAG

remotior-sensus

A Python package that allows for the processing of remote sensing images and GIS data.

geoai-py

GeoAI: Artificial Intelligence for Geospatial Data

sentinelsat

Search and download Copernicus Sentinel satellite images

eodag

Earth Observation Data Access Gateway

omnicloudmask

OmniCloudMask is a Python library for fast, accurate cloud and cloud shadow segmentation in satellite imagery.

spyndex

Awesome Spectral Indices in Python.

cars

CARS is a dedicated and open source 3D tool to produce Digital Surface Models from satellite imaging by photogrammetry.

pydisort

Modern Python & C++ API for the radiative transfer solver DISORT. Parallelized with PyTorch tensors; compile-free with: pip install pydisort.

omniwatermask

Python library for high-accuracy water segmentation in satellite and aerial imagery, combining deep learning with NDWI and vector data for robust detection across multiple sensors and resolutions.