Quality assessment of OpenStreetMap data using trajectory mining

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dc.identifier.uri http://dx.doi.org/10.15488/1035
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/1059
dc.contributor.author Basiri, Anahid
dc.contributor.author Jackson, Mike
dc.contributor.author Amirian, Pouria
dc.contributor.author Pourabdollah, Amir
dc.contributor.author Sester, Monika
dc.contributor.author Winstanley, Adam
dc.contributor.author Moore, Terry
dc.contributor.author Zhang, Lijuan
dc.date.accessioned 2017-01-12T08:35:34Z
dc.date.available 2017-01-12T08:35:34Z
dc.date.issued 2016
dc.identifier.citation 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
dc.description.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. eng
dc.description.sponsorship EU/FP7/Marie Curie Initial Training Network MULTI-POS
dc.language.iso eng
dc.publisher Singapore :Taylor and Francis Ltd.
dc.relation.ispartofseries Geo-Spatial Information Science 19 (2016), Nr. 1
dc.rights CC BY 4.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject OpenStreetMap (OSM) eng
dc.subject Spatial data quality eng
dc.subject trajectory data mining eng
dc.subject data quality eng
dc.subject mapping eng
dc.subject qualitative analysis eng
dc.subject spatial data eng
dc.subject trajectory eng
dc.subject.ddc 004 | Informatik ger
dc.subject.ddc 550 | Geowissenschaften ger
dc.subject.ddc 910 | Geografie, Reisen ger
dc.title Quality assessment of OpenStreetMap data using trajectory mining
dc.type Text
dc.type article
dc.relation.issn 1009-5020
dc.relation.doi https://doi.org/10.1080/10095020.2016.1151213
dc.bibliographicCitation.issue 1
dc.bibliographicCitation.volume 19
dc.bibliographicCitation.firstPage 56
dc.bibliographicCitation.lastPage 68
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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