STAC is creating an ecosystem of interoperable spatiotemporal assets. Learn how Azavea has contibuted and about future steps for the specification.
To deal with issues of apparel facility list data quality and scale quickly and efficiently we need a machine learning tool that can capture the knowledge of domain experts, find commonalities in jumbled text, and confidently compare large lists without the need to compare each individual entry.
Since its launch on March 28, the Open Apparel Registry (OAR) has grown to include over 18,300 facilities in 92 countries. We’ve already heard of a few fascinating use cases where data from the OAR contributed to decision making by brands and facilities.
How do noisy labels affect the accuracy of a deep learning model? We added different amounts of noise to the SpaceNet Vegas buildings dataset and trained some models to find out.
Cloud-Optimized GeoTIFFs (COGs) are geoTIFFs hosted on a cloud or file server, and are optimized for remote reads. They proved useful in a recent project.
Scoring Philadelphia City Council districts on assets and risks using a weighted spatial analysis model in R and Python.
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.