How Radiant MLHub Contributes to Global Action Towards a Sustainable Earth – In The Promise and Peril of a Digital Ecosystem for the Planet, authors Jillian Campbell and David Jensen from the United Nations Environment Programme (UNEP) published an urgent call for action to the world: Create a shared vision that leverages new technologies to manage humanity’s footprints or risk perishing as a consequence of the climate and nature crises. The authors expand on the discussion paper, The Case for a Digital Ecosystem for the Environment: Bringing together data, algorithms, and insights for sustainable development, which was authored through a participatory process led by the UN Science Policy-Business Forum.
The SpatioTemporal Catalog (STAC) is an open standard for exchanging catalogs of raster and vector data. The goal of the standard is to increase “ the interoperability of searching for satellite imagery.” The potential applications of the analysis of satellite imagery are far-reaching. Yet, few are engaging with the multitude of data available.
A major impediment is the difficulty of searching and working with the data-the variety of formats and descriptions can flummox even the most experienced of users.
The past couple of years has seen some major steps forward on geospatial interoperability. The trend in OGC towards open collaboration, JSON + REST focus, and OpenAPI specs that started with WFS 3 is sweeping through most all the core specifications. They recently held a successful hackathon, which resulted in agreement on the core ‘building blocks’ that form the ‘OGC API,’ with WFS 3 evolving to become the ‘OGC API — Features’ specification. As the core pieces settle, there is still lots of interesting work happening with the spec, in extensions that enable implementors to match the functionality of previous WFS versions, like Filters, advanced Queries, reprojection, transactions and more.
It is our pleasure to introduce Dr. Hamed Alemohammad, Chief Data Scientist with Radiant Earth Foundation. Dr. Alemohammad is a technical leader and researcher with extensive expertise and knowledge in remote sensing and imagery techniques, and statistical and machine learning models for geospatial and big data analytics. With a proven record of developing new algorithms for multi-spectral satellite and airborne observations and analyzing them to infer actionable insights, he is spearheading Radiant MLHub’s open repository of Earth observation training data and ML models.
Radiant MLHub is democratizing ML data and models, and, diversifying EO applications. At its core, Radiant MLHub provides an open source “Hub” for discovery and access of thematic training data and models, which are necessary to innovate for sustainable development globally.
A core goal of Radiant Earth Foundation is to raise awareness around innovation in geospatial analytics with a particular focus on ML and EO for the global development community. In this spirit, we present Illuminate, a collection of specially-selected courses and other educational resources.
Last month I had the opportunity to present the architecture behind tiles.rdnt.io: Customer Showcase: Exploiting Multi-Region Data Locality with Lambda@Edge — AWS Online Tech Talks.tiles.rdnt.io is interesting for a few reasons: 1) it dynamically renders map tiles for imagery sources anywhere on the internet, 2) it’s entirely serverless — the tiler itself is implemented as a Python Lambda function, and 3) it’s replicated worldwide to reduce latency when rendering and network egress costs for imagery providers.
Data drives decisions. Whether it’s the number on a scale signaling the need to diet or a satellite image showing the extent of flooding for disaster response, data, imagery, and the resulting analyses they enable guide valuable insights and actions.
Radiant Earth Foundation was founded on the premise that much of the world’s best data and imagery was difficult to find and even more difficult to use because of access issues, making these valuable assets stranded and underutilized.
Chris Holmes is pleased to share two pieces of news about the SpatioTemporal Asset Catalogs Spec. The first is that we now have a website! The goal of the website is to be a much more approachable set of explanations than the specification itself. Having the specification live on GitHub was done on purpose to make it more accessible to developers, but it can be intimidating to non-developers. Putting up the website is indeed a milestone, signaling that STAC is maturing enough to welcome a wider audience.
A big thanks goes to David Gavin of Digital Earth Australia for doing all the initial website copy and styling at the 3rd STAC Sprint, with his ‘outreach’ group. We are hoping to have another outreach group do comparable tasks at the 4th Sprint, though the group at the last sprint set a very high bar.
As part of the Amazon Sustainability Data Initiative, Hamed Alemohammad, Chief Data Scientist at Radiant Earth was invited to share how the organization is using open data and the Amazon Web Services (AWS) Cloud to support the global development community.
Machine learning in support of the SDGs
To effectively leverage open EO data and analytics in support of the SDGs, we turn raw EO data into insights that can guide the decisions required to create a sustainable future. Machine learning is an important part of that process but has one major drawback – the lack of geo-diverse training datasets. Radiant Earth is actively working to fill that gap.