Skip to main content

A tool to extract the microsoft building footprints with user defined boundary.

Project description

Version Views PyPI version PyPI Downloads DOI

msfootprint: A Python package for extracting Microsoft's global building footprints based on user-defined boundaries

This tool allows users to retrieve microsoft global building footprint data based on a specified boundary (such as a shapefile or GeoJSON). The footprints are then saved as Geopackage file to a specified output directory.

The computational engine of msfootprint leverages PySpark and split large areas into smaller tiles for parallel processing. Each tile is processed concurrently, significantly reducing execution time. PySpark's distributed architecture ensures efficient data handling and fault tolerance, enabling fast and reliable downloading of large geospatial datasets.

Features

  • Supports multiple boundary input file formats: .shp, .gpkg, .kml, .geojson.
  • Allows users to specify a boundary and retrieve building footprint data for a specific country or region or small study area.
  • Faster downloading, feasible for large scale data download.
  • Compatible with Google Earth Engine (GEE)/ required GEE authentications.

Usage

It needs a GEE account to access the data.

To install the required dependencies, run the following:

pip install msfootprint

Once installed, import it in notebook or any python compiler.

import msfootprint as msf

Initialize all the variables

#Import all necessary things
import pathlib as Path
boundary_shp = Path('./shapefile_directory')
out_dir = Path('./output_directory')

#Import the 3 letter country ISO code of your study area, Eg. For Nepal, NPL, for United States of America it will be 'USA'
countryISO = 'USA'

For US, it is automated, so no need to give statename but for other countries having multiple tables need to follow aforementioned step

Now, run the main script

msf.BuildingFootprintwithISO(countryISO, boundary_shp, out_dir)

It will save the building footprint as geojson format in designated location.

Cite this Work

If you use msfootprint in your work, please cite it as follows:

Dhital, S. (2025). msfootprint: A Python package for extracting Microsoft's global building footprints based on user-defined boundaries (v0.1.24). Zenodo. https://doi.org/10.5281/zenodo.14597326

in BibTex,

@software{dhital2025msfootprint,
  author       = {S. Dhital},
  title        = {msfootprint: A Python package for extracting Microsoft's global building footprints based on user-defined boundaries},
  version      = {v0.1.24},
  year         = {2025},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.14597326},
  url          = {https://doi.org/10.5281/zenodo.14597326}
}

For Any Information

Feel free to reach out to me: Supath Dhital
Email: sdhital@crimson.ua.edu

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

msfootprint-0.1.26.tar.gz (12.9 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

msfootprint-0.1.26-py3-none-any.whl (12.9 MB view details)

Uploaded Python 3

File details

Details for the file msfootprint-0.1.26.tar.gz.

File metadata

  • Download URL: msfootprint-0.1.26.tar.gz
  • Upload date:
  • Size: 12.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.5 CPython/3.11.7 Darwin/24.3.0

File hashes

Hashes for msfootprint-0.1.26.tar.gz
Algorithm Hash digest
SHA256 bbdc08ef2a79efe1932b9fb2dc8ffdab44d2db1b58de24581fba28c5e84a7512
MD5 d7c81263a15aba17bfb6552e19d0da3e
BLAKE2b-256 a8bc24e343c2c96cd8431febd9308877f766895ef07b49590c69e5b997f07185

See more details on using hashes here.

File details

Details for the file msfootprint-0.1.26-py3-none-any.whl.

File metadata

  • Download URL: msfootprint-0.1.26-py3-none-any.whl
  • Upload date:
  • Size: 12.9 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.5 CPython/3.11.7 Darwin/24.3.0

File hashes

Hashes for msfootprint-0.1.26-py3-none-any.whl
Algorithm Hash digest
SHA256 61aec8c41e7b4f9cd066b35b1808d6cb472c0cb56394d62a77a83a0a03171a03
MD5 5941acde73a115c469a548cf768815c6
BLAKE2b-256 428f90501ee5c18f1f85475951fa166f4821a472fbd8df78ba9460976e5c570f

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page