Machine Learning, News

Data Labeling Contest – Cloud Native Geospatial Sprint

Machine Learning, News

Radiant Earth Foundation Awarded Cooperative Agreement from NASA

Machine Learning, News

Radiant Earth Foundation Releases the Benchmark Training Data “LandCoverNet” for Africa

LandCoverNet is an open-access global land cover classification training dataset with satellite image pixels labeled for seven land cover classes.

Radiant Earth Foundation is proud to announce the release of “LandCoverNet,” a human-labeled global land cover classification training dataset. Available for download on Radiant MLHub, the open geospatial library, LandCoverNet will enable accurate and regular land cover mapping allowing for timely insights into natural and anthropogenic impacts on the Earth. This release contains data across Africa, which accounts for ~1/5 of the global dataset.

Machine Learning, News

Radiant MLHub Hosts STAC-Compliant SpaceNet’s Datasets

Founded in 2016 to accelerate open source geospatial machine learning, SpaceNet is a nonprofit organization that runs data challenges and releases the training datasets, baseline algorithms, winning algorithms, and detailed evaluations under an open source license. They have organized six data challenges to date, each focusing on a different problem that applies machine learning to satellite imagery to solve complex mapping problems.

Machine Learning

Open Earth Observation Data in the Age of Machine Learning

The More Things Change, the More They Stay the Same.

Not so long ago, there was just one viable source for Earth imaging data: Landsat, a joint program of NASA and the U.S. Geological Survey whose mission dates back to 1973. Back then, civilian remote sensing was still in its infancy and commercial satellite operators were unheard of. And yet, the nascent professional remote sensing community was galvanized by the promise of what could be. Decades later, civil society has a wealth of commercial and government Earth observation data to analyze, and more is on the way thanks to a dramatic period of innovation whose day is almost here. Innovation fueled by the confluence of available Earth observation data, machine learning methods, cloud computing and an expanding data science workforce that is eager to create new products and solutions, will change everything. And in some respects, nothing.

Machine Learning

Enhancing Earth Observation Solutions in Agriculture with Machine Learning

Machine learning (ML) and Earth observation (EO) are complementary technologies. While EO helps us understand natural and anthropogenic changes on the Earth, ML empowers us to analyze vast amounts of imagery and build new models for EO data that would have been very difficult if not impossible using traditional physical models a few short years ago.

The promise ML and EO hold for agriculture are immense. EO satellites capture data at a global scale, and ML techniques can use these data to map croplands at local, regional, and continental levels, which provide input for farmers and policymakers alike. In particular, the ability to estimate crop yield or detect pest/disease damage during the growing season will be game-changing in addressing food insecurity problems.

Machine Learning

Accessing and Downloading Training Data on the Radiant MLHub API

Machine Learning, News

BigEarthNet Benchmark Archive Now Available on Radiant MLHub

BigEarthNet is a new large-scale benchmark geospatial training data consisting of multi-label land cover classes in ten European countries. 

Radiant Earth Foundation, the leader in enabling access to geospatial training data, is pleased to announce the availability of the BigEarthNet large-scale benchmark archive through Radiant MLHub, the world’s first open library dedicated to Earth observation (EO) training data.

Machine Learning, News

Announcing the Winners of Radiant Earth’s Competition for Crop Detection in Africa

Five Data Scientists emerged as winners of Radiant Earth Foundation’s competition, in partnership with Zindi Africa, to create a machine learning model that classifies farm fields in Kenya by crop type using time series of Sentinel-2 satellite imagery collected during the growing season.

Earth observations provide critical data for agricultural monitoring at scale, and machine learning (ML) techniques are best suited to learn from these data. Yet, building agricultural ML models poses a problem in Africa due to limited training data, as well as add-on hurdles created by the relatively small size of the farms. These difficulties prompted Radiant Earth to design a competition to crowdsource data science skills globally for the best crop detection model.

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…