If you are working with geospatial data visualizations you have probably heard of Cloud Optimized GeoTIFFs and may also have heard of the Meta Raster Format. These formats both provide efficient access to visualization data and have similar goals. The popular GDAL library supports both. So what are the differences, and when would you choose…
If you are working with geospatial data visualizations you have probably heard of Cloud Optimized GeoTIFFs and may also have heard of the Meta Raster Format. These formats both provide efficient access to visualization data and have similar goals. The popular GDAL library supports both. So what are the differences, and when would you choose…
This release of Raster Vision includes bug fixes, an easier setup, improved performance, and the ability to train models off of labels in OSM.
Aster Vision is an open source machine learning library for analyzing huge troves of astrospatial data and finding habitable planets around nearby stars.
We are excited to announce the launch of the Open Apparel Registry, an open source global map of garment producing facilities.
โThe world is one big data problem.โ Andrew McAfee Small satellites are becoming cheaper to launch and operate, and their data is changing how we view the earth. As more commodity satellites enter orbit and begin sending data, the need for smart applications to ingest, archive, process, transform, and distribute that data become more important.…
Idris is a pure, statically-typed functional language with a powerful type system. Let’s explore writing code in Idris using type-driven development and interactive editing.
Tree-shaking lodash can reduce the size of your JavaScript bundle, but it requires that a few conditions are met along the way to implementing it.
TileJSON.io is an open source project by Azavea. It is an easy way to view and share raster tile sets using slippy map endpoints.
We leveraged our ability to process raster imagery, our open sources libraries, and our knack for machine learning to map agricultural fields in Africa.
Accuracy in deep learning models is not as cut and dry as many present it to be. We examine several examples where accuracy is more of a judgment call.