Quality assessment of OpenStreetMap data using trajectory mining

Basiri, A. , Jackson, M., Amirian, P., Pourabdollah, A., 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. (doi: 10.1080/10095020.2016.1151213)

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: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Basiri, Professor Ana
• Authors: Basiri, A., Jackson, M., Amirian, P., Pourabdollah, A., Sester, M., Winstanley, A., Moore, T., and Zhang, L.
• College/School: College of Science and Engineering > School of Geographical and Earth Sciences
• Journal Name: Geo-Spatial Information Science
• Publisher: Taylor & Francis
• ISSN: 1009-5020
• ISSN (Online): 1993-5153
• Published Online: 25 March 2016
• Copyright Holders: Copyright © 2016 Wuhan University
• First Published: First published in Geo-Spatial Information Science 19(1): 56-68
• Publisher Policy: Reproduced under a Creative Commons License