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.
On November 5–7 around 40 people gathered in Arlington, VA, with another 20 or so participating online, for a joint sprint on the SpatioTemporal Asset Catalog (STAC) and OGC API — Features (OAFeat) specifications. It was our 5th STAC sprint, and the second one we’ve done with OAFeat (formerly WFS 3). I’m pleased to report it was a big success, and to me, it felt like the most productive one we’ve had yet. It was awesome to see everyone working away, on many diverse parts of the ecosystem. In this post, I’m going to attempt to do a brief overview of all that happened.
It is our pleasure to introduce Dr. Catherine Nakalembe, Assistant Research Professor at the University of Maryland. Dr. Nakalembe travels the world working with national ministries and regional agencies in East and Southern Africa to monitor agriculture with Earth observation (EO).
As Lead of the NASA Harvest Eastern Africa-Hub program and part of the NASA Harvest and SERVIR Global Applied Science Team, she conducts remote sensing training in the use of EO tools to assess and forecast crop conditions. Her EO capacity building portfolio includes the government ministries in Kenya, Rwanda, Tanzania, Uganda, and Mali, as well as regional agencies such as the Regional Centre For Mapping Resource For Development (RCMRD) and IGAD Climate Prediction and Applications Center.
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.
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.