`odc‐geo` intro draft
The odc-geo library provides powerful tools for geospatial data manipulation, including working with coordinate reference systems, grid definitions, and spatial transformations.
The library integrates closely with the Python geospatial ecosystem (including Shapely and pyproj), providing projection-aware geometry classes that simplify complex geospatial operations. Geo-registered raster analysis is supported through an .odc. extension that is automatically added to data loaded using the Open Data Cube or rioxarray - exposing important geospatial metadata and a variety of useful and performant geospatial analysis tools.
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Vector and raster reprojection: Efficiently reproject data between CRSs using high-performance reprojection utilities powered by
Dask -
Geospatial metadata handling: Inspect and manipulate geo-registered raster metadata using a standardised
GeoBoxpixel grid model -
Spatial grid definition: Generate precise
GridSpecanalysis tile grids for seamless large-scale analysis - Cloud-optimised data: Export geo-registered raster data into optimised cloud-optimised GeoTIFF files
- Spatial analysis: Tools for rasterising, masking, clipping vector and raster data
- Interactive visualisation: Plot and explore spatial vector and raster data on an interactive map
Updating this wiki: If you notice anything incorrect or out of date in this wiki, please feel free to make an edit!
License: All code in this repository is licensed under the Apache License, Version 2.0. Digital Earth Australia data is licensed under the Creative Commons by Attribution 4.0 license.
Contact: If you need assistance with any of the Jupyter Notebooks or Python code in this repository, please post a question on the Open Data Cube Discord chat or on the GIS Stack Exchange using the open-data-cube tag (you can view previously asked questions here). If you would like to report an issue with any notebook, you can file one on Github.