Machine learning on satellite imagery is revolutionizing disaster relief. What does ethical machine learning mean in this field?
STAC is creating an ecosystem of interoperable spatiotemporal assets. Learn how Azavea has contibuted and about future steps for the specification.
We trained remote computer vision workers to provide data labeling for machine learning projects. Here’s what we learned.
Raster Foundry and GeoTrellis now support WMS and WCS standards, enabling streamlined workflows that utilize multiple data sources.
As a 10% time project, we replicated a small piece of the Raster Foundry backend code in Haskell, using servant for the REST interface and postgresql-simple for database interaction.
Aster Vision – A New Open Source Framework for Deep Learning on Astrospatial Imagery and Space Exploration
Aster Vision is an open source machine learning library for analyzing huge troves of astrospatial data and finding habitable planets around nearby stars.
Azavea is pleased to announce the release of Raster Vision, a new open source framework for deep learning on satellite and aerial imagery.
Learn about our methods and results leveraging our RasterVision deep learning project to predict land use in satellite images of the Amazon.