Quality assessment of OpenStreetMap data using trajectory mining

Basiri, A, Jackson, M, Amirian, P, Pourabdollah, A ORCID: https://orcid.org/0000-0001-7737-1393, Sester, M, Winstanley, A, Moore, T and Zhang, L, 2016. Quality assessment of OpenStreetMap data using trajectory mining. Geo-spatial Information Science, 19 (1), pp. 56-68. ISSN 1009-5020

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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.

Item Type:	Journal article
Publication Title:	Geo-spatial Information Science
Creators:	Basiri, A., Jackson, M., Amirian, P., Pourabdollah, A., Sester, M., Winstanley, A., Moore, T. and Zhang, L.
Publisher:	Taylor & Francis on behalf of Wuhan University
Date:	2016
Volume:	19
Number:	1
ISSN:	1009-5020
Identifiers:	Number Type 10.1080/10095020.2016.1151213 DOI
Rights:	© 2016 Wuhan University. Published by Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Divisions:	Schools > School of Science and Technology
Record created by:	Jill Tomkinson
Date Added:	23 Mar 2018 16:59
Last Modified:	23 Mar 2018 16:59
URI:	https://irep.ntu.ac.uk/id/eprint/33086

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