Learn with Tech Resources

Join the Radiant Earth Spot the Crop Data Challenge to predict crop types in Western Cape South Africa using satellite image time-series from July 5 - September 5. Organized in collaboration with the Western Cape Department of Agriculture in South Africa, and with support from the convening sponsor GIZ FAIR Forward program, the competition consists of two parallel tracks. In track 1, participants will use the time-series of Sentinel-2 multi-spectral data as input to their model. In track 2, both Sentinel-2 and Sentinel-1 data are required as input. For each track, the awards are $4,000, $2,800, and $2,000 for the first, second, and third prizes, respectively, thanks to additional support from platinum sponsor Computer Vision for Global Challenges and gold sponsor, Descartes Labs.

An accurate and timely diagnostic model for tropical cyclone intensity can save lives and property. Join this global competition to predict tropical cyclone wind speed in near-real-time for a chance to win up to $13,000 in cash + $18,000 in Azure credits.  This competition ended on Feb. 2, 2021

This training of trainers bootcamp will cover topics related to use of machine learning techniques on Earth observations. Participants are required to attend all training sessions and work on assignments during the bootcamp. There will be a total of 5 days of training sessions within the two week period. Each participant is then expected to train five other individuals within four months after the bootcamp and report that back to the organizing team. The follow up training is a requirement to receive your certificate of participation in the bootcamp.

Webinar | September 1 | 9 AM EDT

Speakers Joe Morrison, Azavea's GroundWork product specialist, and Hamed Alemohammad, Radiant Earth Foundation's chief data scientist, will talk about how to generate your training data using GroundWork and export and discover it in Radiant MLHub.

Radiant Earth Foundation hosted an international expert workshop to discuss how best to use machine learning techniques on NASA’s Earth Observation data and address environmental challenges. This workshop was sponsored by the NASA Earth Science Data Systems (ESDS). 

• Read the Workshop Report (PDF) to learn about the challenges, potential solutions, and best practices for using EO data in ML workflows identified by expert participants 
• Watch the recorded expert presentations

In this online meeting, the proposed FIVE best practice criteria that make ground reference data ready for Earth observation machine learning models are discussed. Co-hosted by Radiant Earth and CGIAR, the meeting took place on Tuesday, April 21 at 10:00 AM ET.  You can read Radiant Earth's short write up on the proposed criteria, or click below to watch the recording of the discussions.