Today, Radiant Earth Foundation announced the registration of a Spatio-Temporal Asset Catalog (STAC)-compliant version of SpaceNet’s high-quality geospatial labeled datasets for roads and buildings on Radiant MLHub. Radiant MLHub is the world’s first cloud-based open library dedicated to Earth observation training data for machine learning algorithms. The updated dataset catalog is also available through SpaceNet’s data registry.
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.
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 (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.