The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 219–225, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-219-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 219–225, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-219-2020

  06 Aug 2020

06 Aug 2020

CLASSIFICATION OF RAILWAY ASSETS IN MOBILE MAPPING POINT CLOUDS

M. Corongiu1, A. Masiero1,2, and G. Tucci1 M. Corongiu et al.
  • 1Dept. of Civil and Environmental Engineering, University of Florence, via di Santa Marta 3, Florence 50139, Italy
  • 2Interdepartmental Research Center of Geomatics (CIRGEO), University of Padova, Viale dell’Università 16, Legnaro (PD) 35020, Italy

Keywords: Railway, Mobile Laser Scanning, Mobile Mapping, Deep Learning Classification, Point Cloud Segmentation

Abstract. Nowadays, mobile mapping systems are widely used to quickly collect reliable geospatial information of relatively large areas: thanks to such characteristics, the number of applications and fields exploiting their usage is continuously increasing. Among such possible applications, mobile mapping systems have been recently considered also by railway system managers to quickly produce and update a database of the geospatial features of such system, also called assets. Despite several vehicles, devices and acquisition methods can be considered for the data collection of the railway system, the predominant one is probably that based on the use of a mobile mapping system mounted on a train, which moves all along the railway tracks, enabling the 3D reproduction of the entire railway track area.

Given the large amount of data collected by such mobile mapping, automatic procedures have to be used to speed up the process of extracting the spatial information of interest, i.e. assets positions and characteristics.

This paper considers the problem of extracting such information for what concerns cantilever and portal masts, by exploiting a mixed approach. First, a set of candidate areas are extracted and pre-processed by considering certain of their geometric characteristics, mainly extracted by using eigenvalues of the covariance matrix of a point neighborhood. Then, a 3D modified Fisher vector-deep learning neural net is used to classify the candidates. Tests on such approach are conducted in two areas of the Italian railway system.