Welcome to cuSpatial’s documentation!#
cuSpatial provides functionality for fast GPU-based spatial index and join functions via point-in-polygon, a pathing library for trajectory identification and reconstruction, and accelerated GIS functions like haversine distance and grid projection. The icing-on-the-cake of cuSpatial is that we integrate neatly with GeoPandas and RAPIDS cudf.
This enables you to take advantage of all of the features of GeoPandas, plus blisteringly fast GPU acceleration for pandas-like functionality in cudf and spatial functions that perform quickly on millions and tens of millions of geometries.
GeoArrow#
cuSpatial proposes a new GeoArrow format from the fruit of discussions with the GeoPandas team. GeoArrow is a packed columnar data format for the six fundamental geometry types: Point, MultiPoint, Lines, MultiLines, Polygons, and MultiPolygons. MultiGeometry is a possibility that may be implemented in the future. GeoArrow uses packed coordinate and offset columns to define objects, which enables very fast copies between CPU, GPU, and NIC.
Any data source that is loaded into cuSpatial via cuspatial.from_geopandas()
can then take advantage of cudf’s GPU-accelerated Arrow I/O routines.
Read more about GeoArrow format in GeoArrow Format.