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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Earth observation processing framework for machine learning in Python
| Date | Stars |
|---|---|
| 2026-07-31 | 1240 |
| 2026-08-02 | 1241 |
| 2026-08-06 | 1242 |
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[](https://pypi.org/project/eo-learn) [](https://anaconda.org/conda-forge/eo-learn) [](https://pypi.org/project/eo-learn) [](https://github.com/sentinel-hub/eo-learn/actions) [](https://eo-learn.readthedocs.io) [](https://github.com/sentinel-hub/eo-learn/blob/master/LICENSE) [](https://pepy.tech/project/eo-learn) [](https://pepy.tech/project/eo-learn) [](https://hub.docker.com/r/sentinelhub/eolearn) [](https://codecov.io/gh/sentinel-hub/eo-learn) [](https://zenodo.org/badge/latestdoi/135559956) <img align="right" src="docs/source/figures/eo-learn-logo.png" alt="" width="300"/> # eo-learn **eo-learn makes extraction of valuable information from satellite imagery easy.** The availability of open Earth observation (EO) data through the Copernicus and Landsat programs represents an unprecedented resource for many EO applications, ranging from ocean and land use and land cover monitoring, disaster control, emergency services and humanitarian relief. Given the large amount of high spatial resolution data at high revisit frequency, techniques able to automatically extract complex patterns in such _spatio-temporal_ data are needed. **`eo-learn`** is a collection of open source Python packages that have been developed to seamlessly access and process _spatio-temporal_ image sequences acquired by any satellite fleet in a timely and automatic manner. **`eo-learn`** is easy to use, it's design modular, and encourages collaboration -- sharing and reusing of specific tasks in a typical EO-value-extraction workflows, such as cloud masking, image co-registration, feature extraction, classification, etc. Everyone is free to use any of the available tasks and is encouraged to improve the, develop new ones and share them with the rest of the community. **`eo-learn`** makes extraction of valuable information from satellite imagery as easy as defining a sequence of operations to be performed on satellite imagery. Image below illustrates a processing chain that maps water in satellite imagery by thresholding the Normalised Difference Water Index in user specified region of interest.  **`eo-learn`** _library acts as a bridge between Earth observation/Remote sensing field and Python ecosystem for data science and machine learning._ The library is written in Python and uses NumPy arrays to store and handle remote sensing data. Its aim is to make entry easier for non-experts to the field of remote sensing on one hand and bring the state-of-the-art tools for computer vision, machine learning, and deep learning existing in Python ecosystem to remote sensing experts. ## Package Overview **`eo-learn`** package is structured into several modules according to different functionalities. Some modules contain extensions under the `extra` subfolder. Those modules typically require additional package dependencies which don't get installed by default, since they are usually very specific to the task. The modules are: - **`core`** - The main module which implements basic building blocks (`EOPatch`, `EOTask` and `EOWorkflow`) and commonly used functionalities. - **`coregistration`** - Tasks which deal with image co-r
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Matej Aleksandrov · Google · Germany
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c2e1ed69a2fb6f05, llm:Repository description and readme: 'Earth observation processing framework for machine learning in Python', topics include 'eo-data', 'eo-research', 'machine-learning'. Readme: 'eo-learn makes extraction of valuable information from satellite imagery easy' and mentions spatio-temporal data and EO applications.
matched fp:c2e1ed69a2fb6f05, llm:Repository description and readme: 'Earth observation processing framework for machine learning in Python', topics include 'eo-data', 'eo-research', 'machine-learning'. Readme: 'eo-learn makes extraction of valuable information from satellite imagery easy' and mentions spatio-temporal data and EO applications.
matched fp:c2e1ed69a2fb6f05, llm:Repository description and readme: 'Earth observation processing framework for machine learning in Python', topics include 'eo-data', 'eo-research', 'machine-learning'. Readme: 'eo-learn makes extraction of valuable information from satellite imagery easy' and mentions spatio-temporal data and EO applications.