Machine Learning, News

Data Labeling Contest – Cloud Native Geospatial Sprint

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Radiant Earth Foundation Awarded Cooperative Agreement from NASA

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Radiant Earth Foundation Leadership Transition

Radiant Earth Foundation today announces the transition of executive leadership effective September 1, 2020. Anne Hale Miglarese, the founder and CEO, will exit the organization as a full-time employee and remain as a member of the Board of Directors. Hamed Alemohammad, Radiant Earth’s Chief Data Scientist, will also assume the duties of Executive Director of the organization and join the Board of Directors. Board member Jerry Johnston will become the Chairman of the Board of Directors.

Machine Learning, News

Radiant Earth Foundation Releases the Benchmark Training Data “LandCoverNet” for Africa

LandCoverNet is an open-access global land cover classification training dataset with satellite image pixels labeled for seven land cover classes.

Radiant Earth Foundation is proud to announce the release of “LandCoverNet,” a human-labeled global land cover classification training dataset. Available for download on Radiant MLHub, the open geospatial library, LandCoverNet will enable accurate and regular land cover mapping allowing for timely insights into natural and anthropogenic impacts on the Earth. This release contains data across Africa, which accounts for ~1/5 of the global dataset.

Machine Learning, News

Radiant MLHub Hosts STAC-Compliant SpaceNet’s Datasets

Founded in 2016 to accelerate open source geospatial machine learning, SpaceNet is a nonprofit organization that runs data challenges and releases the training datasets, baseline algorithms, winning algorithms, and detailed evaluations under an open source license. They have organized six data challenges to date, each focusing on a different problem that applies machine learning to satellite imagery to solve complex mapping problems.

Machine Learning, News

BigEarthNet Benchmark Archive Now Available on Radiant MLHub

BigEarthNet is a new large-scale benchmark geospatial training data consisting of multi-label land cover classes in ten European countries. 

Radiant Earth Foundation, the leader in enabling access to geospatial training data, is pleased to announce the availability of the BigEarthNet large-scale benchmark archive through Radiant MLHub, the world’s first open library dedicated to Earth observation (EO) training data.

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STAC 1.0-beta.1 Released!

The SpatioTemporal Asset Catalog community is incredibly proud to announce the release of STAC 1.0! If you want to get technical it’s 1.0-beta.1, which means that everything is not yet completely locked in. And it’s just the core specs, as we’ve split off the STAC API into its own repository, and its 1.0-beta.1 release will follow. But this is a huge milestone, as it symbolizes that the community has worked through every known issue and desired improvement. It is the beginning of the final stabilization steps, to ensure STAC will be a stable core that people can build on for years and even decades to come.

The reason we are calling it a ‘beta’ release is so that the specification is not so set that we can’t take additional feedback as we push to get it much more widely adopted. The goal between beta.1 and 1.0.0 is to update every piece of software that has implemented STAC, as well as upgrade all the existing STAC Catalogs to the latest, so we are sure our changes work for everyone.

Machine Learning, News

Announcing the Winners of Radiant Earth’s Competition for Crop Detection in Africa

Five Data Scientists emerged as winners of Radiant Earth Foundation’s competition, in partnership with Zindi Africa, to create a machine learning model that classifies farm fields in Kenya by crop type using time series of Sentinel-2 satellite imagery collected during the growing season.

Earth observations provide critical data for agricultural monitoring at scale, and machine learning (ML) techniques are best suited to learn from these data. Yet, building agricultural ML models poses a problem in Africa due to limited training data, as well as add-on hurdles created by the relatively small size of the farms. These difficulties prompted Radiant Earth to design a competition to crowdsource data science skills globally for the best crop detection model.

Machine Learning, News

NASA ML4EO Workshop 2020

In January, Radiant Earth Foundation hosted an international expert workshop to discuss how best to use machine learning (ML) techniques on NASA’s Earth Observation (EO) data and address environmental challenges. In particular, generation and usage of training datasets for ML applications using EO were discussed. 

The recordings from the Radiant Earth NASA #ML4EO Workshop lightning talks are now available on our YouTube channel! Thank you again to all our workshop participants. Click the link below to watch the exciting expert presentations. 

This workshop was sponsored by the NASA Earth Science Data Systems program

Machine Learning, News

Microsoft AI for Earth and SpaceNet Training Data Now Available on Radiant MLHub

Radiant Earth Foundation today announced the availability of Microsoft AI for Earth’s Chesapeake Bay Land Cover and SpaceNet’s Roads and Buildings training datasets through Radiant MLHub, an open digital training data repository that debuted earlier this week with “crop type” labels for major crops in Kenya, Tanzania, and Uganda.

Designed to encourage widespread data collaboration, Radiant MLHub allows anyone to access, store, register and/or share open training datasets for high-quality Earth observations.