Greater Impact with Machine Learning

Radiant MLHub is an open library for geospatial training data (and soon machine learning models) 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.

Radiant MLHub Python Client was released in March 2021. You can use the client to search data on Radiant MLHub and easily download them to your Python environment. Checkout our tutorials repository on GitHub for Jupyter Notebook examples for using the Python Client.

Radiant Earth is developing the Geospatial Machine Learning Model Catalog (GMLMC) specification. GMLMC will empower repositories of ML models that users can access to find existig ML models for various geosptial applications. If you are interested in this topic, checkout GMLMC on GitHub to explore the ways you can contribute to it.

Datasets

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For more details check registry.mlhub.earth

Crop Type Datasets

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

Benin

Central Asia

Ghana

Kenya

Rwanda

South Sudan

Tanzania

Uganda

LandCoverNet

Chesapeake Bay Land cover

BigEarthNet

Open Cities AI Challenge Dataset

Western USA Live Fuel Moisture

Tropical Cyclone Wind Estimation Competition

SEN12-FLOOD

SpaceNet

7 datasets available on Radiant MLHub registry

FAQ

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What’s the purpose of Radiant MLHub?

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.

How do I get data?

Accessing data currently requires using the API. Go to dashboard.mlhub.earth, sign up (or sign in if you have already registered) and get your API access token. API documentation is available at docs.mlhub.earth. You can read more about accessing data in this guide.

Do I have to pay to use the data?

No! Radiant MLHub is open-access, meaning that you can access and download the data for free. Most of the datasets have a CC-BY-4.0 license; however, you should consult each dataset’s license before use as in some cases there may be specific license restrictions.

How can I find each dataset’s license?

License information are included in each dataset’s metadata under `license` key. You can see an example in this tutorial for retrieving the license.

Where do you get data?

Datasets are generated by Radiant Earth Foundation or our partners and other members of the community. Partners that have created training datasets store them either in our cloud repository or in a different cloud location but register it on Radiant MLHub, meaning that it can be accessed through our API.

Can I contribute data?

Yes! If you have a complete training dataset currently on the cloud that you want registered through Radiant MLHub, a dataset that you want to be hosted, or labels that you think can be used to create training data, please contact ml@radiant.earth.

Why should I share my data?

Open training data allows for faster and more collaborative advancement of machine learning models. Training data tends to be expensive to create, making it very hard for people without access to data to build machine learning applications. This is especially a problem when conducting research in developing countries where data access and availability is limited. By sharing training data on an open-access repository, efforts can be focused on improving machine learning models and creating new datasets where needed, rather than duplicating existing data.

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!

Join Radiant MLHub Slack

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