Multi-granular street network representation towards quality assessment of OpenStreetMap data

OpenStreetMap(OSM) has recently emerged as a promising solution to the challenging task of creating an accurate and up-to-date map of the fast changing world. However, as this database is primarily created by amateurs, the quality of its data is unknown unless a comparison to ground truth is performed. Road networks form a significant feature of OSM and in the past several researchers have tried to assess their quality by using techniques which usually involve some sort of referencing to maps captured by the corresponding national mapping agency. However, finding some automated machine learning based solution to solve this problem of quality assurance seems to be a natural choice. Nevertheless, in order to apply any algorithm successfully, an appropriate representation of the underlying data is a prerequisite. The two predominant street network representations (namely, the primal and dual) fail to represent data in a form suitable for machine learning. Therefore, this paper presents a simple and intuitive representation for street networks where information like street name and category is easily available. This representation offers the combined advantages of primal and dual forms and allows for an efficient application of machine learning algorithms towards quality assessment of street networks datsets.