Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1511–1516, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1511-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1511–1516, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1511-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  05 Jun 2019

05 Jun 2019

UPDATING A ROAD NETWORK DATASET EXPLOITING THE RESULTS OF SEMANTIC SEGMENTATION TECHNIQUES APPLIED TO STREET-LEVEL IMAGERY

A. Ajmar1, E. Arco2, P. Boccardo2, F. Giulio Tonolo3, and J. Yoong4 A. Ajmar et al.
  • 1ITHACA, Via Pier Carlo Boggio 61, 10138 Torino, Italy
  • 2Politecnico di Torino – DIST, Torino, Italy
  • 3Politecnico di Torino, Department of Architecture and Design, Italy
  • 4Mapillary Inc.. 134 North 4th Street, Brooklyn NY 11249-3296, USA

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.