Machine Learning

A Guide for Collecting and Sharing Ground Reference Data for Machine Learning Applications

Machine learning (ML) applications for Earth observation (EO) can use currently available data that are collected via surveys for empirical research to investigate applied sciences or conduct socio-economic analysis. However, the chances of those survey data being incomplete are high. Many ML applications on EO require ground reference data, which are accurate observations of some property on the ground and can be used as a label or description of what a potential overhead image¹ represents.

It is, of course, possible to label images remotely using online platforms like OpenStreetMap…

Machine Learning

Which Data Science Event is Right for You?

As data-driven approaches become more integrated with global development missions like food security, practitioners need to stay up to date with data science methodologies and best practices. Trying to keep up with this rapidly evolving field can feel overwhelming. Luckily, there are numerous capacity-building data science events that provide a great way to stay abreast of new trends and developments.

Data science events are all structured in a similar participant-driven style, but each type has key differences and lends itself to distinct data science skillset and goals. In this article …

Machine Learning

Regional Training Data are Essential for Building Accurate Machine Learning Models

Thinking that having familiarity with Las Vegas in the United States would be enough to navigate the streets of Khartoum, Sudan sounds far-fetched. Yet, many machine learning models use Western-centric training data to predict features from satellite imagery in places as culturally and economically diverse as Bangladesh, Uganda, or Honduras.

Advances in computer vision and machine learning (ML) are improving the ability to accurately extract insights from frequent and high-resolution satellite imagery, shedding light on global development and progress …

Community Voices

Olayinka Fadahunsi: Open Data Opportunities and Challenges in Africa

It is our pleasure to introduce Olayinka Fadahunsi, a Data Scientist with Stanbic IBTC Bank in Lagos, Nigeria and focuses on predictive customer models in personal and business banking. A graduate from the University of Lagos with a degree in Electrical and Electronics Engineering, he also moonlights as a Data Scientist on Zindi, Africa’s first data science competition platform that is focused on solving the continent’s most pressing problems. As one of the top data scientists on Zindi, Mr. Fadahunsi is enthusiastic to use his data science skills to solve real-world challenges …

Standards

STAC 0.9.0 Release — our final major release before 1.0-beta

I am pleased to announce that the incredible STAC community has just released version 0.9.0! This work on the release began in earnest during the 5th STAC Sprint that took place in early November. Having everyone in person enabled us to discuss all the major issues remaining, and we managed to get to decisions on all of them and got to at least draft pull requests of each. The last couple of months have been spent refining those and getting all the little details right, including two ‘release candidates’ — drafts that the community could give feedback on. You can see the full list of improvements in the changelog, and I’ll detail the highlights below.

Machine Learning, News

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.

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.