In this webinar, the first-place winners of the Radiant Earth Spot the Crop data challenge on Zindi present their winning solutions to prediction crop types in Western Cape, South Africa. Raphael Kiminya from Kenya won the track that used satellite image time-series of Sentinel-2 multi-spectral data as input to his model. MG Ferreira from South Africa and Tien-Dung LE from Belgium teamed up to win the track that used Sentinel 2 and Sentinel-1 (radar) satellite data as input to their model. Mike Wallace from the Western Cape Department of Agriculture and Phillip Olbrich from GIZ will provide brief remarks, followed by a Q&A session. Watch the recording.

The call for nominations is open to any individual or team building agricultural-related applications for Africa. The Radiant MLHub Impact Award is a new annual prize organized by Radiant Earth Foundation. The award will recognize an individual or team contributing to real-world applications that address agriculture issues with geospatial training data found on Radiant MLHub. Launched in 2019, Radiant MLHub is an open library dedicated to Earth observation training data and machine learning models.

The Radiant MLHub Impact Award aims to raise awareness about geospatial data to tackle the urgent challenges of climate change, promote local solutions to local problems, and facilitate greater collaboration across organizations and individual practitioners.

Radiant Earth and the Regional Centre for Mapping of Resources for Development (RCMRD) partnership aims to strengthen local geospatial and machine learning expertise for addressing global challenges in Africa.

The Locus Charter promotes responsible practice in the use of location data across all sectors.
We are pleased to announce that we’ve joined the Locus Charter, together with the American Geographical Society (AGS), Association for Geographical Information (AGI), Environmental Information Systems Africa (EIS — Africa), the National Institute of Statistics and Geography (INEGI — Mexico), Open Geospatial Consortium (OGC), PLACE, Royal Geographical Society (RGS) and the Royal Institution of Chartered Surveyors (RICS).

The Locus Charter is a collection of ten international principles to support the ethical and responsible practice of individuals and organizations when using location data.

Designed to strengthen practitioners’ local capacity and skills in support of creating impactful machine learning applications.
We have the pleasure of introducing the online Machine Learning for Earth Observations (ML4EO) course. ML4EO technologies present a game-changing opportunity to identify and address unique, complex, and emerging challenges at local, regional, and global scales more accurately and more quickly. The plethora of EO data, combined with ML techniques, can help humanity see, understand, and respond more effectively to a rapidly changing world. But data scientists specializing in ML for EO are underrepresented, especially in Africa. Therefore, in May, we partnered with the Ugandan AI and data science research group at Makerere University to train a community of African regional experts collaborating on solutions to the continent’s needs and problems using ML4EO technologies.