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Sensors 2016, 16(9), 1510;

Using Crowdsourced Trajectories for Automated OSM Data Entry Approach

The Nottingham Geospatial Institute, The University of Nottingham, Nottingham NG7 2TU, UK
Ordnance Survey GB, Southampton SO16 0AS, UK
Department of Computer Science, Maynooth University, Maynooth W23 F2H6, Ireland
Author to whom correspondence should be addressed.
Academic Editor: Yu Wang
Received: 13 July 2016 / Revised: 11 August 2016 / Accepted: 23 August 2016 / Published: 15 September 2016
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The concept of crowdsourcing is nowadays extensively used to refer to the collection of data and the generation of information by large groups of users/contributors. OpenStreetMap (OSM) is a very successful example of a crowd-sourced geospatial data project. Unfortunately, it is often the case that OSM contributor inputs (including geometry and attribute data inserts, deletions and updates) have been found to be inaccurate, incomplete, inconsistent or vague. This is due to several reasons which include: (1) many contributors with little experience or training in mapping and Geographic Information Systems (GIS); (2) not enough contributors familiar with the areas being mapped; (3) contributors having different interpretations of the attributes (tags) for specific features; (4) different levels of enthusiasm between mappers resulting in different number of tags for similar features and (5) the user-friendliness of the online user-interface where the underlying map can be viewed and edited. This paper suggests an automatic mechanism, which uses raw spatial data (trajectories of movements contributed by contributors to OSM) to minimise the uncertainty and impact of the above-mentioned issues. This approach takes the raw trajectory datasets as input and analyses them using data mining techniques. In addition, we extract some patterns and rules about the geometry and attributes of the recognised features for the purpose of insertion or editing of features in the OSM database. The underlying idea is that certain characteristics of user trajectories are directly linked to the geometry and the attributes of geographic features. Using these rules successfully results in the generation of new features with higher spatial quality which are subsequently automatically inserted into the OSM database. View Full-Text
Keywords: OpenStreetMap; completeness; spatial data quality; crowdsourcing; trajectory data mining OpenStreetMap; completeness; spatial data quality; crowdsourcing; trajectory data mining

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Basiri, A.; Amirian, P.; Mooney, P. Using Crowdsourced Trajectories for Automated OSM Data Entry Approach. Sensors 2016, 16, 1510.

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