UPDATING A ROAD NETWORK DATASET EXPLOITING THE RESULTS OF SEMANTIC SEGMENTATION TECHNIQUES APPLIED TO STREET-LEVEL IMAGERY
Keywords: Road network, Traffic sign, Semantic segmentation, Street-level imagery, Data fusion, Topology
Abstract. Traffic (or road) signs are an important component for applications in the mobility domain. When integrated with a road network, traffic signs, e.g. speed limits, restricted access, breakthrough prohibition signs, provide information that can be exploited in determining impedances, travel times and routing options. Additionally, the availability of a traffic sign geospatial dataset is considered crucial, especially when installation and maintenance tasks are strictly managed by road concessionaires. Unfortunately, public and private road concessionaires not always have this kind of dataset, and its generation could be highly demanding in terms of both human and time resources.
The focus of this paper is on exploring the fit for purpose of semantic segmentation techniques to feed and update existing road network datasets and traffic sign censuses, exploiting free and open mapping initiative like Mapillary (possibly including commercial derivative products) and OpenStreetMap (OSM). More specifically, the authors are analysing the best approaches for integrating the results of map features extraction into road network data with the objective to develop a semi-automated procedure for supporting this task and made it feasible over large urban areas.