Quality assessment of OpenStreetMap data using trajectory mining. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Basiri, Anahid; Jackson, Mike; Amirian, Pouria; Pourabdollah, Amir; Sester, Monika; Winstanley, Adam; Moore, Terry; Zhang, Lijuan
• Is Part Of: Geo-spatial information science. Volume 19:Issue 1(2016)
• Page Start: 56
• Page End: 68
• Publication Date: 2016-01-02
• Subjects: Spatial data quality -- OpenStreetMap (OSM) -- trajectory data mining; Geographic information systems -- Periodicals; Cartography -- Data processing -- Periodicals; Surveying -- Data processing -- Periodicals; Remote sensing -- Periodicals; 526.0285
• DOI: 10.1080/10095020.2016.1151213
• Languages: English
• ISSNs: 1009-5020

Abstract

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.