Our Community Voice for this quarter is Emmanuel Siaw-Darko. He joined Radiant Earth as a Machine Learning Intern after winning third place in the AI4FoodSecurity data challenge for his model to classify crop types in South Africa and Germany. In this Q&A, Emmanuel talks to us about his data science journey and working at Radiant to build baseline models that data scientists can use to compare their algorithms.

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 is working towards his M.Sc. at the intersection of Graph Representation Learning and scientific discovery. The nature of his work sparks due to his interest in contributing his grain of salt to problems that can help tackle climate change.

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.