# eo-learn

Canonical: https://gisforweb.com/tools/eo-learn/

Python framework for modular Earth-observation processing workflows.

eo-learn is a modular Python framework for acquiring and processing spatiotemporal Earth-observation imagery. Its EOPatch, EOTask, and EOWorkflow abstractions support repeatable sequences for Sentinel Hub data, masking, co-registration, feature extraction, sampling, and machine-learning preparation. It bridges remote-sensing workflows and the Python data-science ecosystem.

## Details

- Type: library

- Categories: data-processing, satellite, analysis

- Open source: Yes

- Pricing model: open-source

- License: MIT

- Platforms: linux, macos, windows, docker

## Features

- Compose reusable EOTask processing workflows

- Acquire imagery from Sentinel Hub services

- Apply cloud, snow, and quality masks

- Co-register spatiotemporal image sequences

- Extract features and prepare machine-learning samples

- Read and write GeoTIFF, Zarr, and vector data

## Project sources

- Website: https://eo-learn.readthedocs.io/en/latest/

- Documentation: https://eo-learn.readthedocs.io/en/latest/

- Repository: https://github.com/sentinel-hub/eo-learn

Directory descriptions are summaries. Verify current capabilities and terms with the project sources.