The Radiant MLHub API is a STAC compliant API that serves metadata about label items and source imagery and links to download these items.

STAC is an organization of metadata for imagery and labels, making it easy to search for items that match spatial, temporal, or other criteria. At the root level of the STAC API is a list of collections of items. In the Radiant MLHub API, each collection contains items for either source imagery or labels for a dataset. These items are descriptions of source imagery or labels and links to download assets related to these items. Properties found in these item descriptions include spatial extent, temporal extent, band descriptions in the case of optical imagery, label types and label properties in the case of labels, and other information like DOIs and citation examples to reference.

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 …