Description

Radiant MLHub is an open library for geospatial training data that hosts 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. Training datasets include pairs of imagery and labels for different types of machine learning problems including image classification, object detection, and semantic segmentation. Labels are generated from ground reference data and/or image annotation.

Update Frequency

New training data catalogs are added on a rolling basis

License

Access to Radiant MLHub data is free for everyone. Each dataset has its own license (usually CC-BY or CC-BY-SA). View Terms of Service.

Documentation

http://docs.mlhub.earth/

Managed By

See all datasets managed by Radiant Earth Foundation.

Contact

support@radiant.earth

How to Cite

Radiant MLHub was accessed on DATE from https://registry.opendata.aws/radiant-mlhub.

Usage Examples

Tutorials

• Explore wind speed images from Amazon Sustainability Data Initiative (ASDI) hosted on S3 using SageMaker Studio Lab (SMSL) by Frank Rubino
  
  
• How to access Radiant MLHub Data by Radiant Earth
  
• Radiant MLHub Tutorials with Jupyter Notebooks by Kevin Booth
  

Tools & Applications

• Challenge on Computer Vision for Crop Detection from Satellite Imagery by Radiant Earth
  
• Radiant MLHub Dataset Registry by Radiant Earth
  

Publications

• A Guide for Collecting and Sharing Ground Reference Data for Machine Learning Applications by Yonah Bromberg Gaber
  
• Creating a Machine Learning Commons for Global Development by Hamed Alemohammad
  
• Geo-Diverse Open Training Data as a Global Public Good by Hamed Alemohammad