sentinel-hub/eo-learn

Health v2 · maintenance only

Adoption not part of the score

1247 stars · 304 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

eo-learn is a collection of open-source Python packages for accessing and processing spatio-temporal satellite imagery, built around modular, reusable processing tasks. It bridges Earth observation data (e.g. Copernicus Sentinel and Landsat) with machine learning workflows for extracting information from imagery.

Use cases

• process satellite imagery for machine learning
• classify land cover from Sentinel-2 data
• build cloud masking pipelines for Earth observation data
• extract water bodies from satellite images using NDWI
• run feature extraction workflows on spatio-temporal image sequences
• prepare Earth observation datasets for deep learning models

When to choose

• you work with Sentinel, Landsat, or other satellite imagery in Python
• you need modular, reusable processing chains for spatio-temporal EO data
• you want to connect Earth observation data pipelines to ML frameworks
• you need tasks like cloud masking, co-registration, and classification out of the box

When to avoid

• you only need simple one-off raster operations without workflow structure
• your project deals with non-geospatial imagery
• you need real-time streaming image processing rather than batch workflows
• you require a GUI-based GIS tool instead of a Python library

Facets

framework · maturity active

machine-learning image-processing etl data-science workflow-automation machine-learning data-science image-processing python cross-platform earth-observation satellite-imagery sentinel geospatial remote-sensing spatio-temporal copernicus landsat automation docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="sentinel-hub/eo-learn")