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.

News, Standards

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.

Machine Learning, News

Radiant Earth Foundation Releases World’s First Open Repository for Geospatial Training Data

WASHINGTON, Dec. 09, 2019 (GLOBE NEWSWIRE) — To make geospatial information more accessible to data scientists who are working on global priorities like food insecurity, Radiant Earth Foundation has launched Radiant MLHub, the world’s first cloud-based open library dedicated to Earth observation training data for use with machine learning algorithms, it announced today.

Machine Learning, News, Thought Leadership

The Many Meanings of ‘Open’

Open Data, Open Source, and Open Standards
Over the past decade, much has been said about open data, open source software, and open standards. So much, in fact, that many people have begun to use the terms interchangeably. But open data, open source, and open standards are not synonymous and should not be conflated.

The confusion poses a challenge for many organizations, in particular, those which lack technological expertise but nevertheless work on global issues that seek out “open” digital solutions. In this article, we define the parameters of open data, open source, and open standards, and identify the key differences between them.

Editorials, News

The stage of maturity — Earth observation in a new era of space exploration

2019 First Quarter EO Market News Round-Up – Space commerce is enjoying a renaissance period mainly due to technological advances that have dramatically decreased cost and increased data and related services. A $17+ billion market (and growing), today’s space industry is on the verge of entering maturity — the stage of self-discovery, boldness, and adventure.

The maturing space industry is evident with players in both private and public sectors accelerating the recent advances in science and technology that makes operating in space more viable for commercial and research interests. This year thus far, the European Space Agency (ESA) tested its new 3D printed rocket thrust chamber to help design more efficient rocket engines.