Machine Learning, News

Machine Learning for Earth Observation Market Map

Meet the 100+ organizations that focus on machine learning applications with satellite data

Building geospatial machine learning applications involve many dependable moving parts, from accessing Earth observation (EO) data, labeling imagery, and generating training data to creating and developing models and running analytics. A growing list of organizations from various sectors are providing solutions and services to advance these applications. Who can help you build machine learning applications, identify patterns from your data, or run your crowdsourcing campaign? What organizations are providing software or a platform that you can utilize to develop your machine learning model?

Machine Learning, News

Cloud-Spotting at a Million Pixels an Hour

Jon Engelsman won the Best Quality Labeler award for our recent Data Labeling Contest. We asked him to detail his approach and workflow.

I recently attended the Cloud Native Geospatial Outreach Day, a virtual event designed to “introduce STACCOG, and other emerging cloud-native geospatial formats and tools to new audiences.” As part of the outreach day, co-sponsors PlanetMicrosoftAzavea, and Radiant Earth teamed up to host a week-long data labeling contest. This friendly competition had contestants race to manually label the shapes of clouds across a large selection of satellite images from around the world. The contest’s ultimate goal was to generate a crowd-sourced collection of high-quality labeled images, data that can be used to train accurate cloud detection models.

Machine Learning, News

Announcing the Winners of the Data Labeling Contest

Earlier this month, we organized a data labeling contest as part of the Cloud Native Geospatial Outreach Day sponsored by Planet, Microsoft, and Azavea. The contest was designed as a crowdsourcing campaign to encourage the global community to contribute to open-access training data catalogs. Participants were asked to identify cloudy pixels in Sentinel-2 scenes.

The labeling contest was conducted on GroundWork, Azavea’s annotation tool designed for geospatial data. We were amazed by the high participation worldwide and the community’s excitement to help develop a large-scale accurate cloud detection training dataset. In the end, 231 users around the world signed up and labeled 75,645 tasks, which equates to about 2 million km2 of classified Sentinel-2 imagery.

Machine Learning, News

Data Labeling Contest – Cloud Native Geospatial Sprint

Machine Learning, News

Radiant Earth Foundation Awarded Cooperative Agreement from NASA

News

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.

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.