This release brings some exciting new functionality to Raster Vision making it easier than ever to apply machine learning to satellite imagery.
We highlight some major changes below. For a full list of changes, see the changelog.
Raster Vision can now consume imagery from STAC APIs
See tutorial: Imagery from STAC API + labels from OSM.
You can now pass an Xarray DataArray representing STAC items (e.g. from the results of a STAC API search query) to the newly-added XarraySource to get a RasterSource that is compatible with the rest of Raster Vision.
Raster Vision can now work with temporal data
See tutorial: Working with time-series of images
Raster Vision can now read in time series of images via TemporalMultiRasterSource and XarraySource. These temporal RasterSources can be used as drop-in replacements for normal RasterSources.
Raster Vision can now export and use models in ONNX format
The model-bundles produced by Raster Vision now additionally include the model in the ONNX format, and Raster Vision will use an ONNX runtime to make predictions if the environment variable RASTERVISION_USE_ONNX=1 is set.
Useful links
For more information, and to get started using Raster Vision, view the resources below.
Website: https://rastervision.io/
GitHub repo: https://github.com/azavea/raster-vision
Documentation: https://docs.rastervision.io/en/0.21/index.html
Tutorials: https://docs.rastervision.io/en/0.21/usage/tutorials/index.html
Changelog: https://docs.rastervision.io/en/0.21/changelog.html#raster-vision-0-21
