Five Data Scientists emerged as winners of Radiant Earth Foundation’s competition, in partnership with Zindi Africa, to create a machine learning model that classifies farm fields in Kenya by crop type using time series of Sentinel-2 satellite imagery collected during the growing season.

Earth observations provide critical data for agricultural monitoring at scale, and machine learning (ML) techniques are best suited to learn from these data. Yet, building agricultural ML models poses a problem in Africa due to limited training data, as well as add-on hurdles created by the relatively small size of the farms. These difficulties prompted Radiant Earth to design a competition to crowdsource data science skills globally for the best crop detection model.

In January, Radiant Earth Foundation hosted an international expert workshop to discuss how best to use machine learning (ML) techniques on NASA’s Earth Observation (EO) data and address environmental challenges. In particular, generation and usage of training datasets for ML applications using EO were discussed. 

The recordings from the Radiant Earth NASA #ML4EO Workshop lightning talks are now available on our YouTube channel! Thank you again to all our workshop participants. Click the link below to watch the exciting expert presentations. 

This workshop was sponsored by the NASA Earth Science Data Systems program

Radiant Earth Foundation today announced the availability of Microsoft AI for Earth’s Chesapeake Bay Land Cover and SpaceNet’s Roads and Buildings training datasets through Radiant MLHub, an open digital training data repository that debuted earlier this week with “crop type” labels for major crops in Kenya, Tanzania, and Uganda.

Designed to encourage widespread data collaboration, Radiant MLHub allows anyone to access, store, register and/or share open training datasets for high-quality Earth observations.

WASHINGTON, Dec. 09, 2019 (GLOBE NEWSWIRE) — To make geospatial information more accessible to data scientists who are working on global priorities like food insecurity, Radiant Earth Foundation has launched Radiant MLHub, the world’s first cloud-based open library dedicated to Earth observation training data for use with machine learning algorithms, it announced today.