An AI-based approach to identify spatio-temporal contribution patterns and their impact on OpenStreetMap data quality

Category

Published in:

Abstracts of the ICA
Vol. 7.2024 (02.09.2024), art. no. 6, p. 1-2

Volume:

7.2024

Publication date:

02.09.2024

URN:

urn:nbn:de:0307-20250822-002-0

DOI:

10.5194/ica-abs-7-6-2024

Language:

English

Type of Resource:

Text

Extent:

1 Online-Ressource (Seite 1-2, 754,72 kB) : 1 Textdatei (PDF)

Keywords:

Volunteered Geographic Information; VGI; OpenStreetMap^GND; Data Quality; Intrinsic Parameters; freiwillige erhobene geografische Informationen; Datenqualität^GND; intrinsischer Parameter

DDC subject of DNB:

520 Astronomy & allied sciences

URL:

https://doi.org/10.5194/ica-abs-7-6-2024

URL:

https://ica-abs.copernicus.org/articles/7/6/2024/

Link to Raw Object:

https://ica-abs.copernicus.org/articles/7/6/2024/ica-abs-7-6-2024.pdf

Hosting institution:

Federal University of Paraná (UFPR] [BR]

Hosting institution:

Heidelberg University [DE]

Hosting institution:

Internationale Forschungseinrichtungen

Statement of responsibility:

Nathan Damas Antonio, Silvana Camboim, Alexander Zipf

Bibliographic notes:

Published in: Abstracts of the ICA, Volume 7, 2024 - European Cartographic Conference – EuroCarto 2024; 9–11 September 2024, TU Wien, Vienna, Austria; Editors: Georg Gartner, Florian Ledermann, and Andrea Binn | https://ica-abs.copernicus.org/articles/7/index.html