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.
| Original language | English |
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| Title of host publication | 12th International Conference on Geographic Information Science, GIScience 2023 |
| Editors | Roger Beecham, Jed A. Long, Dianna Smith, Qunshan Zhao, Sarah Wise |
| Publisher | Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing |
| ISBN (Electronic) | 9783959772884 |
| DOIs | |
| State | Published - Sep 2023 |
| Event | 12th International Conference on Geographic Information Science, GIScience 2023 - Leeds, United Kingdom Duration: Sep 12 2023 → Sep 15 2023 |
Publication series
| Name | Leibniz International Proceedings in Informatics, LIPIcs |
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| Volume | 277 |
| ISSN (Print) | 1868-8969 |
Conference
| Conference | 12th International Conference on Geographic Information Science, GIScience 2023 |
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| Country/Territory | United Kingdom |
| City | Leeds |
| Period | 09/12/23 → 09/15/23 |
Funding
© Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/ doe-public-access-plan). This material is based upon the work supported by the U.S. Department of Energy under contract no. DE-AC05-00OR22725. The author gives thanks to Daniel Adams and Jessica Moehl for their thoughtful review and advice. © Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/ doe-public-access-plan). This material is based upon the work supported by the U.S. Department of Energy under contract no. DE-AC05-00OR22725.
Keywords
- Building footprints
- Data comparison
- Open data