Community Voices – Radiant Earth Foundation

Data Challenge Winner: Q&A with Christian Ayala Lauroba

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 and crop types as ground truth input. It was organized by Planet, TUM/DLR, and Radiant Earth from 4 October to 19 December 2021. 188 competed for a chance to win one of the fantastic prizes that included internships, subscriptions to various platforms, and scholarships. In this Q&A, we sat down with Christian Ayala Lauroba from Spain, the leader of the team that won first place in both the …

Community Voices

Radiant MLHub Spotlight Q&A: Victor Faraggi

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 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.

Community Voices

Radiant MLHub Spotlight Q&A: Renate Thiede

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

Renate is an alumnus of Radiant Earth’s first virtual ML4EO training of trainers bootcamp that focused on using machine learning with satellite data. The bootcamp ran from May 3–14, 2021, thanks to a grant from the GIZ FAIR Forward- Artificial Intelligence for all program, which the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) implements on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ).

In this Q&A, we sat down with Renate to discuss her journey combining statistics, geospatial data, and AI. She also shares her experience participating in the ML4EO Bootcamp.

Community Voices

Data Challenge Winners: Q&A with MG Ferreira and Tien-Dung LE

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.

Community Voices

Data Challenge Winner: Q&A with Raphael Kiminya

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.

Community Voices

Radiant MLHub Spotlight Q&A: Gedeon Muhawenayo

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.

Community Voices

Radiant MLHub Spotlight Q&A: Macroecology and Society Lab

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.

Community Voices, Machine Learning

Igor Ivanov: Harnessing Machine Learning Skills to Reduce Damages from Tropical Storms

A conversation with the First Place winner of the Radiant Earth Tropical Cyclone Wind Estimation Data Competition

We recently announced the Radiant Earth Tropical Cyclone Wind Estimation Data Competition winners, a contest designed to build a machine learning (ML) model to improve NASA IMPACT’s Deep Learning-based Hurricane Intensity Estimator. Seven hundred thirty-three participants leveraged NOAA’s Geostationary Operational Environmental Satellites (GOES) imagery to estimate the wind speeds of storms at different points in time using satellite images captured throughout a storm’s life cycle. In this Q&A, we sat down with Igor Ivanov from Ukraine, winner of the first place Development Seed Award, to talk about his journey to become a data scientist and winning the contest.

Community Voices

Ashiraf Nsibambi Kyabainze: Technology and Social Entrepreneurship in Uganda

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

Meet Ashiraf Nsibambi Kyabainze, the founder of At HAUSE Limited, is a Ugandan entrepreneur working on African technology. The recipient of several awards, including Africa’s Top Young Entrepreneurs Award (RUFORUM) in 2018 and the Mandela Washington Fellowship for Young African Leaders Initiative Network agribusiness champion in 2019, Ashiraf founded At HAUSE to support agricultural and agribusiness workers by improving their packaging with insect-resistant solutions. At HAUSE’s business model reduces crop waste all while ensuring better financial returns for farmers.

Community Voices, Machine Learning

Celebrating Women Leading the ML4EO Community

Meet the rising stars of women around the world at the forefront of machine learning for Earth observation.

Happy International Women’s Day!

Today, we celebrate the women who break barriers and expand the frontiers of machine learning for Earth observation. This essential field can help us understand the planet’s ecosystem, its different elements, interactions, and changes.

These 15 leading women were selected from 56 outstanding nominations from the ML4EO community. The Radiant Earth Foundation selection committee created a set of criteria to rank the nominees.