News – Radiant Earth Foundation

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.

Community Voices

Radiant MLHub Spotlight Q&A: Macroecology and Society Lab

Building application-ready tools and data for policymakers, resource managers, and other scientists to understand global dynamics in human-environment systems.
Our Community Voices for this quarter are Dr. Carsten Meyer, Mr. Ruben Remelgado, Dr. Steffen Ehrmann, and Ms. Caterina Barrasso from the German Centre for Integrative Biodiversity Research (iDiv) Macroecology and Society Lab. They are working on several projects to detect and understand global dynamics in human-environment systems, focusing on human land use, its underlying societal drivers, and its ecological consequences. The research team uses numerous datasets from Radiant MLHub to model crop suitability layers, which will inform the systematic downscaling of crop statistics into pixel-scale crop type classifications.

Machine Learning, Standards

Discoverable and Reusable ML Workflows for Earth Observation (Part 1)

Using STAC to catalog machine learning training data.
Researchers and data scientists are increasingly combining Earth observation (EO) with ground truth data from a variety of sources to build faster, more accurate machine learning (ML) models to gain valuable insights in domains ranging from agriculture to autonomous navigation to ecosystem health monitoring. These models are integrated into analytic pipelines that generate on-the-fly predictions at scale. The accuracy of these inferences are then evaluated using well-defined validation metrics and the results used to improve the performance of the original model in a continuous feedback loop.

If this sounds like a complex process, that’s because it is! Ad-hoc techniques for handling these workflows may work well within a single organization, but can lead to a bewildering array of algorithms and data for end-users.

Standards

STAC API Version 1.0.0-beta.2 released!

The SpatioTemporal Asset Catalog (STAC) Community is pleased to announce the release of version 1.0.0-beta.2 of the STAC API specification! A big thanks to Phil Varner for leading this release, and to everyone else who pitched in.

What is STAC API? As we are welcoming many new people to the STAC community with the core STAC 1.0.0 release it’s probably worth explaining what this ‘API’ release is all about. STAC originally started from a desire to make a common API to help interoperability between satellite data providers, but soon evolved to focusing on the core JSON language to enable more general geospatial interoperability. The STAC repository initially contained both the API specification along with the three ‘core’ specs (Item, Catalog & Collection). But it was clear that the API really depends on the core, and expands it with additional functionality, so after version 0.9.0 we decided to split STAC into two repositories.

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.

Standards

STAC 1.0.0: Software Ecosystem Updates

This post continues the STAC 1.0.0 announcement and updates on the community and spec, with an in-depth dive into one part of what we call the ‘STAC Ecosystem’. This is the ever-growing set of libraries, clients, and servers that rely on the STAC specification to build towards our goal of interoperability. The advanced state of this ecosystem is one of the things I’m most proud of with STAC, as one of the earliest goals was to have as much real-world validation as possible. And the coolest thing is that it feels like the momentum is just starting to really hit an inflection point, with more and more tools supporting STAC.

So this post will give an overview of the current state of STAC-related software, and where our ‘STAC 1.0.0 Initiative’ sponsor money has enabled us to accelerate. And in the next post, I’ll share what else we’re planning to fund, towards a vision of ‘STAC Ecosystem 1.0.0’. That’s the point when there are complete tools, with all the appropriate tutorials and documentation, so that anyone can easily create or consume STAC. Our goal is that most people will be able to do so without having to ever read the specification. It will just be the foundation that enables a network of interoperable data.

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.

Standards

STAC 1.0.0: Spec and Community Updates

While we just announced STAC 1.0.0, the initial post did not contain any of the typical information we normally include in a STAC release announcement. So in this post, we’ll dive into the details of what changed in the specification, including extensions, as well as some of the community highlights. And this will just be one of several posts diving deeper into STAC topics, including the wider ecosystem of tools.

Spec Changes — As always, the most definitive list of what has changed between versions of STAC is the CHANGELOG, but it’s always good to draw attention to the highlights. So I’ll summarize here the major changes, building on the changes summarized in the 1.0.0-rc.1 release, including rc.2, rc.3, and rc.4.

Standards

STAC Specification 1.0.0 Released!

The SpatioTemporal Asset Catalog (STAC) community is pleased to announce the release of version 1.0.0. It’s safe to say we’re all quite proud of the release, as it’s been a large community effort for over three years. For those who haven’t heard of STAC, it provides a common language to describe a range of geospatial information, so it can more easily be indexed and discovered. You can learn more at the stacspec.org website, which we aim to update in the coming months to be an even better learning resource.
A Stable Foundation –I’m not going to go into all the details of the release for this post, but I’m aiming to do a series of blog posts to highlight all that has happened, share what’s next, and highlight all the amazing tools in the community. The one thing I do want to share is what a 1.0.0 release means for us. Our goal for the core STAC specification is to provide a foundational layer for the emerging ‘Cloud Native Geospatial’ ecosystem. We believe that the future of geospatial information is fully online and in the cloud, and STAC aims to help connect diverse data into a network of information about our planet (and even other planets).

Machine Learning

Publish your training data on Radiant MLHub for NeurIPS 2021

Submissions to the new Datasets and Benchmarks track require data documentation and availability on an open repository.

Organizers of the NeurIPS 2021 conference recently announced a new track for Datasets and Benchmarks. This is a significant development for a major machine learning (ML) conference to highlight the importance of data in developing algorithms for real-world problems. We at Radiant Earth Foundation welcome this initiative and applaud the organizers for establishing this new track.

In recent years, there have been many discussions and arguments to incentive ML researchers to work on real-world problems. One of those incentive mechanisms is the opportunity to publish a paper in a peer-reviewed conference, and getting recognition for working on these problems. The new track at NeurIPS is a necessary step to realize these incentives.