License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.35
URN: urn:nbn:de:0030-drops-189309
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18930/
Go to the corresponding LIPIcs Volume Portal

Gonzales, Jack Joseph

Building-Level Comparison of Microsoft and Google Open Building Footprints Datasets (Short Paper)

pdf-format:
LIPIcs-GIScience-2023-35.pdf (0.9 MB)

Abstract

Large-scale datasets of building footprints are a crucial source of information for a variety of efforts. In 2023, the general public benefits from open access to multiple sources of building footprints at the country scale or larger, such as those produced by Microsoft and Google. However, none of the available datasets have attained complete global coverage, and researchers and analysts may need to combine multiple sources to assemble a complete set of building footprints for their area of interest or choose between overlapping sources, requiring an understanding of the differences between different building sources. This paper presents a method to closely examine the quality of different building footprint sources by matching corresponding buildings across datasets, using building footprints in Ethiopia published by Microsoft and Google as an example set.

BibTeX - Entry

@InProceedings{gonzales:LIPIcs.GIScience.2023.35,
  author =	{Gonzales, Jack Joseph},
  title =	{{Building-Level Comparison of Microsoft and Google Open Building Footprints Datasets}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{35:1--35:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18930},
  URN =		{urn:nbn:de:0030-drops-189309},
  doi =		{10.4230/LIPIcs.GIScience.2023.35},
  annote =	{Keywords: Open data, Building footprints, Data comparison}
}

Keywords: Open data, Building footprints, Data comparison
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI