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Radiant MLHub

Open Library for Earth Observations Machine Learning.

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Greater Impact with Machine Learning

Radiant MLHub is an open library for geospatial training data to advance machine learning applications on Earth Observations. It serves as a resource for a community of practice, giving data scientists benchmarks they can use to train and validate their models and improve its performance.

Radiant MLHub hosts open training datasets generated by Radiant Earth Foundation's team as well as other training data catalogs contributed by Radiant Earth’s partners. Radiant MLHub is open to anyone to access, store, register and/or share their training datasets for high-quality Earth observations. All of the training datasets are stored using a SpatioTemporal Asset Catalog (STAC) compliant catalog, and exposed through a common API. In future, model hosting and Python client applications will be added to MLHub services.

Training datasets include pairs of imagery and labels for different types of ML problems including image classification, object detection, and semantic segmentation. Labels are generated from ground reference data and/or annotation of imagery.

Datasets

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For more details check registry.mlhub.earth

African Crops

The following crop type datasets are currently hosted on Radiant MLHub. Check each dataset's documentation on the API for more information.

Kenya

Surveyed in 2019 growing season by PlantVillage team, this dataset includes 319 plots. Crops types in this dataset are: Banana, Bean, Cabbage, Cassava, Cowpea, Fallowland, Groundnut, Maize, Millet, Sorghum, Soybean, Sugarcane, Sweet Potato, Tomato.

Tanzania

This dataset contains 392 plots provided by Great African Food Company. Data are collected in 2018 growing season and include Bush Bean, Dry Bean, Safflower, Sunflower, White Sorghum, Yellow Maize.

Uganda

This dataset contains 234 plots provided by Dalberg Data Insights. Data are collected in 2017 growing season and include Maize and Sorghum.

FAQ

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Radiant MLHub on Slack

Join Radian MLHub community on Slack to be part of this collaborative open source group. If you are looking for new training data or models, or are interested in collaborating with us to develop new ones, get in touch!

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