Identifying sustainable business models for open machine learning ready Earth observation repositories — When Gedeon Jean* first realized the power of Earth observation (EO) data to detect environmental changes, he was mesmerized. As a Machine Learning Research Engineer, he saw the potential of combining machine learning (ML) and EO to develop diverse predictive applications for Rwanda, his native country. Rwanda is increasingly experiencing…

A conversation with the First Place winning team’s lead of the AI4FoodSecurity Data Challenge. — Hosted on ESA’s AI4EO platform, the AI4FoodSecurity data challenge brought together participants worldwide to find the best machine learning/AI solutions for crop identification using Planet Fusion data and Sentinel-1 and -2 data. The challenge covered two areas of interest, Germany and South Africa, with high-quality cadastral data on field boundaries…

Accelerating climate change applications with machine learning models and remote sensing data. — Meet Victor Faraggi, our Community Voice for the second quarter of 2022. Victor is a Computer Science Engineering graduate from the University of Chile. There, he’s been part of the Teaching Staff of multiple courses, such as Algorithm Design and Analysis, Introduction to Data Mining, and Machine Learning. Currently, Victor…

How Radiant MLHub strengthens the data collection to analytics pipeline for agriculture projects. — By Hamed Alemohammad, Executive Director and Chief Data Scientist at Radiant Earth Foundation Radiant Earth Foundation is strengthening geospatial machine learning (ML) workflows for organizations working on agriculture projects by streamlining the process, from ground reference data collection to insight analytics, through Radiant MLHub.

These 14 women are pushing the boundaries of science using machine learning for Earth observation worldwide. — Happy International Women’s Day! We are excited to introduce this year’s cohort of women who contribute to society’s betterment using machine learning for Earth observation (ML4EO). The role of ML4EO in helping to create a society that is balanced socially, ecologically, and environmentally is paramount. But the technologies we humans…

A guide to identifying croplands with reasonable accuracy using a semantic segmentation model. — By Daniel Nwaeze, Data Scientist, Radiant Earth Foundation We developed UNet-Agri, a benchmark machine learning model that classifies croplands using open-access Sentinel-2 imagery at 10m spatial resolution with ground reference data provided by the Western Cape Department of Agriculture in South Africa. This post is a step-by-step walkthrough of how…

The call for nominations is open to any individual or team building agricultural-related applications for Africa — By Amber Myers, Business Development Manager, Radiant Earth Foundation “The winner will receive a $5,000 cash prize to support their work and recognition across our community of practice for their outstanding contribution to problem-solving.” The Radiant MLHub Impact Award is a new annual prize organized by Radiant Earth Foundation. The…

Combining mathematical statistics, geospatial data, and artificial intelligence in support of global development. — Happy New Year! Meet our Community Voice for the first quarter of 2022, Renate Thiede. Renate is a doctoral candidate in spatial statistics at the University of Pretoria, where she teaches part-time. Spatial statistics is a niche area, combining geography and statistical analysis. Renate’s career trajectory as a Spatial Statistician…

A conversation with the First Place winners of the Radiant Earth Spot the Crop XL Data Challenge. — We recently announced the Radiant Earth Spot the Crop Data Challenge winners to predict crop types in Western Cape, South Africa using satellite image time-series. …