https://github.com/geofileops/geofileops

Python toolbox to process large geospatial vector files faster.
https://github.com/geofileops/geofileops

gdal geopackage geopandas geoprocessing geospatial-data geospatial-processing gis large-files python vector

Last synced: 7 months ago
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Python toolbox to process large geospatial vector files faster.

• Host: GitHub
• URL: https://github.com/geofileops/geofileops
• Owner: geofileops
• License: bsd-3-clause
• Created: 2019-08-19T15:44:09.000Z (over 6 years ago)
• Default Branch: main
• Last Pushed: 2025-04-10T11:58:29.000Z (8 months ago)
• Last Synced: 2025-04-12T00:36:41.848Z (8 months ago)
• Topics: gdal, geopackage, geopandas, geoprocessing, geospatial-data, geospatial-processing, gis, large-files, python, vector
• Language: Python
• Homepage: https://geofileops.readthedocs.io/
• Size: 22.8 MB
• Stars: 128
• Watchers: 4
• Forks: 5
• Open Issues: 28
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.txt
  • Citation: CITATION.cff

Awesome Lists containing this project

• Awesome-Geospatial - geofileops - Python toolbox to process large vector files faster. (Python)

README

          
# geofileops 

[![Actions Status](https://github.com/geofileops/geofileops/actions/workflows/tests.yml/badge.svg?branch=main)](https://github.com/geofileops/geofileops/actions/workflows/tests.yml?query=workflow%3ATests) 

[![Coverage Status](https://codecov.io/gh/geofileops/geofileops/branch/main/graph/badge.svg)](https://codecov.io/gh/geofileops/geofileops)

[![PyPI version](https://img.shields.io/pypi/v/geofileops.svg)](https://pypi.org/project/geofileops)

[![Conda version](https://anaconda.org/conda-forge/geofileops/badges/version.svg)](https://anaconda.org/conda-forge/geofileops)

[![DOI](https://zenodo.org/badge/203202318.svg)](https://zenodo.org/doi/10.5281/zenodo.10340100)

Geofileops is a python toolbox to process large vector files faster.

Most typical GIS operations are available: e.g.

[buffer](https://geofileops.readthedocs.io/en/stable/api/geofileops.apply.html#geofileops.buffer), 

[dissolve](https://geofileops.readthedocs.io/en/stable/api/geofileops.apply.html#geofileops.dissolve),

[difference](https://geofileops.readthedocs.io/en/stable/api/geofileops.apply.html#geofileops.difference), 

[intersection](https://geofileops.readthedocs.io/en/stable/api/geofileops.apply.html#geofileops.intersection), 

[union](https://geofileops.readthedocs.io/en/stable/api/geofileops.apply.html#geofileops.union),... 

The spatial operations are tested on geopackage and shapefile input files, but

geopackage is recommended as it will give better performance. General 

[layer](https://geofileops.readthedocs.io/en/stable/reference.html#general-layer-operations)

and [file operations](https://geofileops.readthedocs.io/en/stable/reference.html#general-file-operations) can be used on the file formats supported by 

[GDAL](https://gdal.org/).

The full documentation is available on [readthedocs](https://geofileops.readthedocs.io).

Different techniques are used under the hood to be able to process large files as fast

as possible:

* process data in batches

* subdivide/merge complex geometries on the fly

* process data in different passes

* use all available CPUs

The following chart gives an impression of the speed improvement that can be expected

when processing larger files. The [benchmarks](https://github.com/geofileops/geobenchmark)

typically use input file(s) with 500K polygons, ran on a Windows PC with 12 cores and include I/O.

![Geo benchmark](https://github.com/geofileops/geobenchmark/blob/main/results_vector_ops/GeoBenchmark.png)