Radiant MLHub

Documentation

You can use the Radiant MLHub Python Client to search datasets on Radiant MLHub and easily download them to your Python environment. You can use the API directly for programming languages other than Python.

Tutorials and Notebooks

Radiant MLHub Tutorials repository on GitHub has a series of Jupyter Notebooks for you to get started with using our API. Each notebook has comments and explanations that guides you through each step of accessing the API, searching for data, downloading data and loading data in Python, among others. There are also notebooks specific to each competition that walks you through building a baseline machine learning model.

Radiant Earth Foundation Youtube channel has a series of videos from our training programs and workshops that you can watch to learn more about applications of machine learning on Earth observations, and how to use Radiant MLHub resources for various applications.

Python Client Quick-Start Guide

  1. Get a Radiant MLHub API Key from your Settings page.
  2. Install and configure the MLHub Python Client:
    $ pip install radiant_mlhub
    $ mlhub configure
    API Key: Enter your API key here...
    Wrote profile to /Users/youruser/.mlhub/profiles
  3. Start coding! The radiant_mlhub Python Client documentation has examples and introductory material.

FAQ

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 their performance.

Accessing data currently requires using the API. Sign in and thenget your API key. API documentation is also available on this page. You can read more about accessing data in this guide.

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.

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

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.

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 sign in and browse to Contribute a Dataset.

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.