During this turbulent month, the Earth observation community put their skills and resources towards supporting the emergency response to COVID-19. Experts also continued to push EO advancements, including releasing a new STAC intake driver, soil moisture prediction deep learning model, and image labeler platform.
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…
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 …
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 …
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 …
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.
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
The public release of open datasets in response to the Australian wildfires and Taal volcanic eruption, the retirement of the Rapideye Planet constellation, and the announcements of STAC v0.9.0 and a new Capella SAR constellation all made for a busy and exciting first month of the year in the EO market. Read more below.
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.