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.

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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.

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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.

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Community Voices

Data Labeling Contest: Crowdsourcing a scalable solution to generate labels for satellite imagery

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.

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Community Voices

Zhuangfang NaNa Yi: Building Machine Learning Applications that Empower Policymakers with Insights to Support Vulnerable Communities

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.

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Community Voices

Data Challenge Winners: Identifying African crop types using satellite imagery

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.

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Community Voices, Machine Learning

Olayinka Fadahunsi: Open Data Opportunities and Challenges in Africa

It is our pleasure to introduce Olayinka Fadahunsi, a Data Scientist with Stanbic IBTC Bank in Lagos, Nigeria and focuses on predictive customer models in personal and business banking. A graduate from the University of Lagos with a degree in Electrical and Electronics Engineering, he also moonlights as a Data Scientist on Zindi, Africa’s first data science competition platform that is focused on solving the continent’s most pressing problems. As one of the top data scientists on Zindi, Mr. Fadahunsi is enthusiastic to use his data science skills to solve real-world challenges …

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Community Voices

Catherine Nakalembe: Enhancing Agricultural Productivity with Earth Observation

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.

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Community Voices, Machine Learning

Hamed Alemohammad: Addressing global challenges with models that are faster

It is our pleasure to introduce Dr. Hamed Alemohammad, Chief Data Scientist with Radiant Earth Foundation. Dr. Alemohammad is a technical leader and researcher with extensive expertise and knowledge in remote sensing and imagery techniques, and statistical and machine learning models for geospatial and big data analytics. With a proven record of developing new algorithms for multi-spectral satellite and airborne observations and analyzing them to infer actionable insights, he is spearheading Radiant MLHub’s open repository of Earth observation training data and ML models.

Radiant MLHub is democratizing ML data and models, and, diversifying EO applications. At its core, Radiant MLHub provides an open source “Hub” for discovery and access of thematic training data and models, which are necessary to innovate for sustainable development globally.

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Community Voices

Awa Thiam: Creating Efficient Sustainable Food Systems with Geospatial Data

It is our pleasure to introduce to you Ms. Awa Thiam, Founder of Lifantou and 2018 Mandela Washington Fellow alumna. Lifantou is an innovative social enterprise connecting smallholder farmers with school canteens in Senegal using geospatial data and tools.

African social entrepreneurs are considered essential for social progress in Africa. Often combining innovative technological solutions to address local societal challenges, these social entrepreneurs are not only creating jobs. Perhaps more interesting, their businesses are transforming all facets of society …

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