News

Radiant Earth Foundation Joins the Locus Charter

The Locus Charter promotes responsible practice in the use of location data across all sectors.
We are pleased to announce that we’ve joined the Locus Charter, together with the American Geographical Society (AGS), Association for Geographical Information (AGI), Environmental Information Systems Africa (EIS — Africa), the National Institute of Statistics and Geography (INEGI — Mexico), Open Geospatial Consortium (OGC), PLACE, Royal Geographical Society (RGS) and the Royal Institution of Chartered Surveyors (RICS).

The Locus Charter is a collection of ten international principles to support the ethical and responsible practice of individuals and organizations when using location data.

News

Announcing Radiant Earth’s Online Course on Machine Learning for Earth Observation

Designed to strengthen practitioners’ local capacity and skills in support of creating impactful machine learning applications.
We have the pleasure of introducing the online Machine Learning for Earth Observations (ML4EO) course. ML4EO technologies present a game-changing opportunity to identify and address unique, complex, and emerging challenges at local, regional, and global scales more accurately and more quickly. The plethora of EO data, combined with ML techniques, can help humanity see, understand, and respond more effectively to a rapidly changing world. But data scientists specializing in ML for EO are underrepresented, especially in Africa. Therefore, in May, we partnered with the Ugandan AI and data science research group at Makerere University to train a community of African regional experts collaborating on solutions to the continent’s needs and problems using ML4EO technologies.

News, Standards

Cloud Native Geospatial Ecosystem Community Releases STAC Specification version 1.0.0 to Connect Remote Sensing Data into a Network of Information about the Earth

The SpatioTemporal Asset Catalog (STAC) specification provides a common language to describe a range of geospatial information, so it can more easily be indexed and discovered.

WASHINGTON, June 10, 2021 (GLOBE NEWSWIRE) — The Cloud Native Geospatial Ecosystem Community announces the release of the SpatioTemporal Asset Catalog (STAC) specification version 1.0.0. The STAC specification is an open metadata standard that systemically describes remotely sensed data of the Earth. The specification supports emerging cloud-based geoprocessing engines by allowing spatial data to be indexed and discovered more efficiently. This feature is fundamental when building artificial intelligence applications using Earth imagery.

Machine Learning, News

Radiant MLHub in 2021: Realizing a Data Ecosystem

In December 2019, we publicly launched Radiant MLHub, the first open-access cloud-based repository for geospatial training datasets. Since then, we have continuously published new datasets and expanded the ecosystem around Radiant MLHub.

The idea of Radiant MLHub was born in Spring 2018 after several discussions and feedback from members of the community and funders. We had started a new project to develop a global and geographically diverse land cover training dataset using human verification called LandCoverNet. Soon after the launch of LandCoverNet in 2018, we identified a gap in the ecosystem to facilitate publication and uptake of training datasets in our community. That gap in the data value chain led us to the design and implementation of Radiant MLHub.

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.