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.

In May, we announced the winners of the Radiant Earth Computer Vision for Crop Detection from Satellite Imagery data challenge, which took place in February and March 2020. A total of 440 data scientists signed up for the challenge, representing a wide range of educational backgrounds, institutions, and geographies. While five winners were selected, in this Q&A, we sat down with Karim Amer, the First Place Overall Winner of the Data challenge, and the First Place African Citizen winner, Femi Sotonwa. Our goal is to learn more about the people behind the top scores.

It is our pleasure to introduce Dr. Catherine Nakalembe, Assistant Research Professor at the University of Maryland. Dr. Nakalembe travels the world working with national ministries and regional agencies in East and Southern Africa to monitor agriculture with Earth observation (EO).

As Lead of the NASA Harvest Eastern Africa-Hub program and part of the NASA Harvest and SERVIR Global Applied Science Team, she conducts remote sensing training in the use of EO tools to assess and forecast crop conditions.