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| Rasterio | |
|---|---|
| Name | Rasterio |
| Developer | Mapbox, Christopher A. White, Hobu, OSGeo |
| Initial release | 2015 |
| Programming language | Python, C |
| Operating system | Cross-platform |
| License | MIT License |
Rasterio
Rasterio is an open-source geospatial raster data library implemented for the Python ecosystem that provides reading and writing of GeoTIFF and other raster formats using the GDAL C++ library. It exposes a Pythonic API to perform I/O, affine transformations, reprojection, windowed reading, and metadata management for use in QGIS, ArcGIS, PostGIS, and cloud-native workflows such as Amazon Web Services and Google Cloud Platform. Rasterio is commonly used alongside projects like NumPy, Pandas, Dask, and xarray for geospatial analysis, remote sensing, and cartography.
Rasterio began as a project by contributors at Mapbox and independent developer Christopher A. White to provide a more idiomatic Python wrapper around the GDAL library, simplifying tasks common to users of QGIS and ArcGIS. Early development took place in the context of the broader Open Source Geospatial Foundation community and the OSGeo incubator, with integration into ecosystems that included NumPy and SciPy. Over time, maintainers and contributors from organizations such as Hobu, Mapbox, and academic groups working with NASA and USGS helped expand support for cloud-optimized formats and performance features used in production workflows at companies like Planet Labs and projects such as OpenStreetMap.
Rasterio offers raster I/O for formats supported by GDAL including GeoTIFF, NetCDF, and cloud-optimized variants like Cloud Optimized GeoTIFF and tiled formats used by Esri. Key features include windowed reads and writes for efficient subsetting in workflows common to NASA and NOAA users, coordinate reference system transformations integrating with PROJ for reprojection between EPSG:4326 and EPSG:3857, affine geotransforms compatible with Mapnik and Leaflet, and metadata handling used by USGS and European Space Agency projects. Rasterio ties into numerical stacks such as NumPy and xarray for array manipulation, and scales with Dask and Apache Arrow for distributed processing in cloud environments like Amazon Web Services and Google Cloud Platform.
Rasterio uses a layered architecture where a thin Python layer presents a high-level API while delegating format drivers and low-level I/O to the native GDAL C++ library. The design emphasizes immutable dataset objects, context managers inspired by PEP 343 for safe resource handling, and NumPy-compatible array semantics familiar to users of SciPy and Pandas. Coordinate reference handling relies on integrations with PROJ and spatial reference notions used by EPSG registries, mirroring patterns found in OGR and Fiona. The project follows semantic versioning and continuous integration practices common to repositories hosted on GitHub with contributions reviewed through pull requests and issue tracking similar to workflows used by Django and Flask projects.
Typical Rasterio usage appears in scripts and notebooks alongside Jupyter, where users open a dataset, read windows into NumPy arrays, and perform reprojection to EPSG:3857 before exporting to a GeoTIFF delivered to MapServer or visualized in QGIS. Examples demonstrate reading metadata for datasets produced by Sentinel-2 and Landsat pipelines, performing affine transforms consistent with GDAL utilities, and combining with xarray and Dask for out-of-core mosaicking used by organizations like USGS and European Space Agency. Rasterio-centric tutorials often reference tools such as Rasterio.mask for clipping to vector geometries created in GeoPandas or loaded from PostGIS databases.
Performance characteristics of Rasterio depend largely on the underlying GDAL drivers, tiling, compression schemes like LZW and DEFLATE, and the use of cloud-optimized storage such as Cloud Optimized GeoTIFF on Amazon S3. Benchmarks comparing Rasterio workflows typically evaluate read throughput with different block sizes and resampling algorithms (nearest, bilinear, cubic) and compare single-threaded versus multi-threaded strategies used by GDAL and parallel frameworks like Dask or Apache Spark. Real-world comparisons include ingestion rates observed in projects by Planet Labs, tiling workflows in Mapbox pipelines, and data-serving latencies measured in Carto and MapServer deployments.
Rasterio is part of a broader geospatial Python stack integrating with Fiona for vector I/O, GeoPandas for vector analysis, Shapely for geometry operations, and xarray for multi-dimensional arrays. It interoperates with desktop and server software such as QGIS, ArcGIS, MapServer, and GeoServer, and supports cloud services including Amazon Web Services, Google Cloud Platform, and Microsoft Azure via GDAL's virtual file systems. Rasterio is used in research collaborations with institutions like NASA and NOAA, and in industry by companies such as Mapbox, Planet Labs, and Esri-related tooling.
Development occurs on platforms like GitHub, with contributions from individuals and organizations including Mapbox, Hobu, and independent maintainers. The project follows collaborative open-source governance patterns seen in communities such as Open Source Geospatial Foundation and accepts issues, feature requests, and pull requests via standard continuous integration pipelines used by Travis CI and GitHub Actions. Community support appears on forums and communication channels aligned with those used by Stack Overflow, GIS Stack Exchange, and meetup groups associated with FOSS4G and State of the Map conferences.
Category:Geographic information systems