Quality assessment of OpenStreetMap data using trajectory mining

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Basiri, A.; Jackson, M.; Amirian, P.; Pourabdollah, A.; Sester, M. et al.: Quality assessment of OpenStreetMap data using trajectory mining. In: Geo-Spatial Information Science 19 (2016), Nr. 1, S. 56-68. DOI: http://dx.doi.org/10.1080/10095020.2016.1151213

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/1035

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Sum total of downloads: 303




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Abstract: 
OpenStreetMap (OSM) data are widely used but their reliability is still variable. Many contributors to OSM have not been trained in geography or surveying and consequently their contributions, including geometry and attribute data inserts, deletions, and updates, can be inaccurate, incomplete, inconsistent, or vague. There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data. Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs. This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users. The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature. Using such rules, some sets of potential bugs and errors can be identified and stored for further investigations. © 2016 Wuhan University. Published by Taylor & Francis Group.
License of this version: CC BY 4.0 Unported
Document Type: Text
Publishing status: publishedVersion
Issue Date: 2016
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 226 74.59%
2 image of flag of United States United States 22 7.26%
3 image of flag of China China 16 5.28%
4 image of flag of Russian Federation Russian Federation 7 2.31%
5 image of flag of No geo information available No geo information available 6 1.98%
6 image of flag of Japan Japan 5 1.65%
7 image of flag of United Kingdom United Kingdom 3 0.99%
8 image of flag of Netherlands Netherlands 2 0.66%
9 image of flag of Greece Greece 2 0.66%
10 image of flag of Belgium Belgium 2 0.66%
    other countries 12 3.96%

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