Paper Info
Title
Automated highway tag assessment of OpenStreetMap road networks
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 different semantic classes of streets. This model is subsequently used to accurately determine if a street has been assigned a correct/incorrect semantic class.
Year
DOI
Venue
2014
10.1145/2666310.2666476
SIGSPATIAL/GIS
Keywords
Field
DocType
graphs and networks,applications,verification,openstreetmap,street network analysis,data quality,reliability,machine learning
Spatial analysis,Data science,Data mining,Data quality,Road networks,Street network,Computer science,Artificial intelligence,Machine learning
Conference
Citations 
PageRank 
References 
3
0.76
4
Authors
3
Name
Order
Citations
PageRank
Musfira Jilani1132.70
Padraig Corcoran219123.08
Michela Bertolotto386391.77