Raster Foundry
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Ethical Machine Learning for Disaster Relief: Avoiding the Second Disaster
Machine learning on satellite imagery is revolutionizing disaster relief. What does ethical machine learning mean in this field?
STAC: Creating an Ecosystem of SpatioTemporal Assets
STAC is creating an ecosystem of interoperable spatiotemporal assets. Learn how Azavea has contibuted and about future steps for the specification.
Is This a Crosswalk?: 5 Takeaways from Training a Data Labeling Team
We trained remote computer vision workers to provide data labeling for machine learning projects. Here’s what we learned.
Introducing WMS and WCS Support for GeoTrellis and Raster Foundry
Raster Foundry and GeoTrellis now support WMS and WCS standards, enabling streamlined workflows that utilize multiple data sources.
Lessons in Functional API Development from Haskell’s Servant and Http4s
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.
Mapping Africa: Crowdsourced Machine Learning
We leveraged our ability to process raster imagery, our open sources libraries, and our knack for machine learning to map agricultural fields in Africa.
Raster Vision: A New Open Source Framework for Deep Learning on Satellite and Aerial Imagery
Azavea is pleased to announce the release of Raster Vision, a new open source framework for deep learning on satellite and aerial imagery.
Predicting Land Use in the Amazon using Deep Learning
Learn about our methods and results leveraging our RasterVision deep learning project to predict land use in satellite images of the Amazon.