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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.
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Using the Dedupe Machine Learning Library for Cleaning and Matching Data
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.
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Connecting Data and People with the Open Apparel Registry
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.
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Using Noisy Labels to Train Deep Learning Models on Satellite Imagery
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.
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Developing Countries, Capacity Building, and SDGs
Utilizing earth imagery to achieve all 17 UN SDGs by 2030 will take considerate effort. In order to do this effectively, we must include training and capacity building in our solutions.
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Using Cloud-Optimized GeoTIFFs (COGs) for More Efficient Web App Architecture
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.
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Scoring Philadelphia’s City Council Districts for Children’s Health and Well-being
Scoring Philadelphia City Council districts on assets and risks using a weighted spatial analysis model in R and Python.
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Raster Vision 0.9 Release Candidate
This release of Raster Vision includes bug fixes, an easier setup, improved performance, and the ability to train models off of labels in OSM.
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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.
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