Combining mathematical statistics, geospatial data, and artificial intelligence in support of global development.

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. The competition was organized in two parallel tracks: In track 1, participants used time-series of Sentinel-2 multispectral data as input to their model; In track 2, both Sentinel-2 and Sentinel-1 (radar) data were required as input.

In this Q&A, we sat down with MG Ferreira from South Africa and Tien-Dung LE from Belgium to talk about their journey to becoming data scientists and their approach to tackling the problem. MG and Tien-Dung won the Spot the Crop XL Data Challenge winners that used Sentinel-1 radar and Sentinel-2 multispectral data as input to the model.

A conversation with the First Place winners of the Radiant Earth Spot the Crop 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. The competition was organized in two parallel tracks: In track 1, participants used time-series of Sentinel-2 multispectral data as input to their model; In track 2, both Sentinel-2 and Sentinel-1 (radar) data were required as input.

In this Q&A, we sat down with Raphael Kiminya from Kenya to talk about his journey to becoming a data scientist and his approach to tackling the problem. He won the Spot the Crop Data Challenge that used Sentinel-2 multispectral data as input to the model.

Building machine learning models with open training data for precision agriculture and flood detection in Rwanda.
Our Community Voice for this quarter is Gedeon Muhawenayo, a machine learning research engineer at the Rwanda Space Agency working on machine learning for satellite and aerial image processing.Gedeon is an avid user of the open machine learning training datasets available on Radiant MLHub. In this Q&A, Gedeon talks to us about building machine learning models for precision agriculture and flood detection in Rwanda.

Building application-ready tools and data for policymakers, resource managers, and other scientists to understand global dynamics in human-environment systems.
Our Community Voices for this quarter are Dr. Carsten Meyer, Mr. Ruben Remelgado, Dr. Steffen Ehrmann, and Ms. Caterina Barrasso from the German Centre for Integrative Biodiversity Research (iDiv) Macroecology and Society Lab. They are working on several projects to detect and understand global dynamics in human-environment systems, focusing on human land use, its underlying societal drivers, and its ecological consequences. The research team uses numerous datasets from Radiant MLHub to model crop suitability layers, which will inform the systematic downscaling of crop statistics into pixel-scale crop type classifications.

A conversation about using technology for a smart value chain to boldly impact food insecurity in Uganda

A conversation with the First Place winners of the Data Labeling Contest – In September 2020, we announced the Data Labeling Contest winners. The contest was part of the Cloud Native Geospatial Outreach Day sponsored by Planet, Microsoft, Azavea, and Radiant Earth Foundation. Participants were invited to contribute to open-access training data catalogs by identifying cloudy pixels in Sentinel-2 scenes. Two hundred thirty-one labelers joined the contest, representing a wide range of educational backgrounds, institutions, and geographies. While several awards were given to the top 83 contributions in six categories, in this Q&A, we sat down with Solomon Kica from Uganda and Jhomira Vanessa Loja Zumaeta from Peru, who won the Top Labeler first prize awards. Both winners were selected for the top prize because their scores were incredibly close, a 3.6% difference, and both scores stood out from the rest of the participants.

A conversation about the nuances of applying machine learning algorithms to Earth observation for global development organizations.

It is our pleasure to Dr. Zhuangfang NaNa Yi, a machine learning engineer at Development Seed, supporting international development organizations like UNICEF, the World Bank, and USAID in making data-driven policy decisions. She has extensive experience in applying machine learning algorithms to geospatial and satellite data, from building applications that farmers can use to track crop types and changes to water bodies, mapping forest and measuring food security, and more.