Repository files navigation

Note

cuSpatial depends oncuDF andRMM fromRAPIDS.

cuProj is a new RAPIDS library housed within the cuSpatial repo that provides GPU-accelerated transformations of coordinates between coordinate reference systems (CRS). cuProj is available as of release 23.10 with support for transformations of WGS84 coordinates to and from Universal Transverse Mercator (UTM) 🌐.

To learn more about cuProj, see thePython cuProj README or thec++ libcuproj README.

• cuSpatial User's Guide: Python API reference and guides
• cuSpatial Developer Documentation: Understand cuSpatial's architecture
• Getting Started: Installation options for cuSpatial
• cuSpatial Community: Get help, collaborate, and ask the team questions
• cuSpatial Issues: Request a feature/documentation or report a bug

cuSpatial accelerates vector geospatial operations through GPU parallelization. As part of the RAPIDS libraries, cuSpatial is inherently connected tocuDF,cuML, andcuGraph, enabling GPU acceleration across entire workflows.

cuSpatial represents data inGeoArrow format, which enables compatibility with theApache Arrow ecosystem.

cuSpatial's Python API is closely matched to GeoPandas and data can seamlessly transition between the two:

importgeopandasfromshapely.geometryimportPolygonimportcuspatialp1=Polygon([(0,0), (1,0), (1,1)])p2=Polygon([(0,0), (1,0), (1,1), (0,1)])geoseries=geopandas.GeoSeries([p1,p2])cuspatial_geoseries=cuspatial.from_geopandas(geoseries)print(cuspatial_geoseries)

Output:

0    POLYGON ((0 0, 1 0, 1 1, 0 0))1    POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0))

For additional examples, browse the completeAPI documentation, or check out more detailednotebooks. theNYC Taxi andWeather notebooks make use of cuSpatial.

cuSpatial is constantly working on new features! Check out theepics for a high-level view of our development, or theroadmap for the details!

• Spatial relationship queries (DE-9IM)
• Linestring-Linestring Intersections
• Cartesian distance between any two geometries (ST_Distance)
• Haversine distance
• Hausdorff distance
• Spatial window filtering

• Quadtree indexing
• Spatial joins
• Quadtree-based point-in-polygon
• Quadtree-based point-to-nearest-linestring

• Deriving trajectories from point location data
• Computing distance/speed of trajectories
• Computing spatial bounding boxes of trajectories

Thanks to thefrom_geopandas andto_geopandas functions you can accelerate what cuSpatial supports, and leave the rest of the workflow in place.

---title: Integrating into Existing Workflows---%%{init: { 'logLevel': 'debug', 'theme': 'base', 'gitGraph': {'showBranches': false},            'themeVariables': {'commitLabelColor': '#000000',            'commitLabelBackground': '#ffffff',            'commitLabelFontSize': '14px'}} }%%gitGraph   commit id: "Existing Workflow Start"   commit id: "GeoPandas IO"   commit id: "Geospatial Analytics"   branch a   checkout a   commit id: "from_geopandas"   commit id: "cuSpatial GPU Acceleration"   branch b   checkout b   commit id: "cuDF"   commit id: "cuML"   commit id: "cuGraph"   checkout a   merge b   commit id: "to_geopandas"   checkout main   merge a   commit id: "Continue Work"

CUDA/GPU requirements

• CUDA 11.2+ with acompatible, supported driver
• Volta architecture or newer (Compute Capability >=7.0)

Use theRAPIDS Release Selector, selectingDocker as the installation method. All RAPIDS Docker images contain cuSpatial.

An example command from the Release Selector:

docker run --gpus all --pull always --rm -it \    --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \    -p 8888:8888 -p 8787:8787 -p 8786:8786 \    nvcr.io/nvidia/rapidsai/notebooks:25.04-cuda11.8-py3.12

To install via conda:

Note cuSpatial is supported only on Linux orthrough WSL, and with Python versions 3.10, 3.11, and 3.12.

cuSpatial can be installed with conda from the rapidsai channel:

conda install -c rapidsai -c conda-forge -c nvidia \    cuspatial=25.04 python=3.12 cudatoolkit=11.8

We also provide nightly Conda packages built from the HEAD of our latest development branch.

See theRAPIDS installation documentation for more OS and version info.

To install via pip:

Note cuSpatial is supported only on Linux orthrough WSL, and with Python versions 3.10, 3.11, and 3.12.

The cuSpatial pip packages can be installed from NVIDIA's PyPI index. pip installations require using the matching wheel to the system's installed CUDA toolkit.

• For CUDA 11 toolkits, install the-cu11 wheels
• For CUDA 12 toolkits install the-cu12 wheels
• If your installation has a CUDA 12 driver but a CUDA 11 toolkit, use the-cu11 wheels.

pip install cuspatial-cu12 --extra-index-url=https://pypi.nvidia.compip install cuspatial-cu11 --extra-index-url=https://pypi.nvidia.com

To build and install cuSpatial from source please see thebuild documentation.

If you find cuSpatial useful in your published work, please consider citing the repository.

@misc{cuspatial:25.04,author ={{NVIDIA Corporation}},title ={cuSpatial: GPU-Accelerated Geospatial and Spatiotemporal Algorithms},year ={2023},publisher ={NVIDIA},howpublished ={\url{https://github.com/rapidsai/cuspatial}},note ={Software available from github.com},}