Automated Highway Tag Assessment of OpenStreetMap Road Networks

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Title: Automated Highway Tag Assessment of OpenStreetMap Road Networks
Authors: Jilani, Musfira
Corcoran, Padraig
Bertolotto, Michela
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Date: Nov-2014
Abstract: OpenStreetMap (OSM) has been demonstrated to be a valuable source of spatial data in the context of many applications. However concerns still exist regarding the quality of such data and this has limited the proliferation of its use. Consequently much research has been invested in the development of methods for assessing and/or improving the quality of OSM data. However most of these methods require ground-truth data, which, in many cases, may not be available. In this paper we present a novel solution for OSM data quality assessment that does not require ground-truth data. We consider the semantic accuracy of OSM street network data, and in particular, the associated semantic class (road class) information. A machine learning model is proposed that learns the geometrical and topological characteristics of di erent semantic classes of streets. This model is subsequently used to accurately determine if a street has been assigned a correct/incorrect semantic class.
Funding Details: Irish Research Council
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2014 ACM
Keywords: OpenStreetMapData QualityStreet Network AnalysisMachine Learning
DOI: 10.1145/2666310.2666476
Language: en
Status of Item: Peer reviewed
Conference Details: 22nd ACM SIGSPATIAL (International Conference on Advances in Geographic Information Systems), Dallas, Texas, USA, 4-7 November, 2014
Appears in Collections:Computer Science Research Collection

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