News – Radiant Earth Foundation

NASA ML4EO Workshop 2020

In January, Radiant Earth Foundation hosted an international expert workshop to discuss how best to use machine learning (ML) techniques on NASA’s Earth Observation (EO) data and address environmental challenges. In particular, generation and usage of training datasets for ML applications using EO were discussed.

The recordings from the Radiant Earth NASA #ML4EO Workshop lightning talks are now available on our YouTube channel! Thank you again to all our workshop participants. Click the link below to watch the exciting expert presentations.

This workshop was sponsored by the NASA Earth Science Data Systems program

News

Microsoft AI for Earth and SpaceNet Training Data Now Available on Radiant MLHub

Radiant Earth Foundation today announced the availability of Microsoft AI for Earth’s Chesapeake Bay Land Cover and SpaceNet’s Roads and Buildings training datasets through Radiant MLHub, an open digital training data repository that debuted earlier this week with “crop type” labels for major crops in Kenya, Tanzania, and Uganda.

Designed to encourage widespread data collaboration, Radiant MLHub allows anyone to access, store, register and/or share open training datasets for high-quality Earth observations.

News

Radiant Earth Foundation Releases World’s First Open Repository for Geospatial Training Data

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.

Standards

OGC API — Features + STAC Sprint Recap

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.

Community Voices

Catherine Nakalembe: Enhancing Agricultural Productivity with Earth Observation

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.

News, Thought Leadership

The Many Meanings of ‘Open’

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.

Machine Learning

Creating the Planet’s Digital Ecosystem

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.

Standards

STAC: Creating an Ecosystem of SpatioTemporal Assets — Azavea

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.

Standards

Join STAC Sprint #5 + OGC API — Features hackathon November 5–7

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.

Community Voices

Hamed Alemohammad: Addressing global challenges with models that are faster

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.