Classification of Railway Assets in Mobile Mapping Point Clouds

Abstract

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.

Enhancing Railway Detection by Priming Neural Networks with Project Exaptations

“…Their mixed approach, targeting tunnels, uses support vector machines to segment the rails from the railbed. Similar ideas have also been applied to road infrastructure [19]. Chen et al [20] achieved remarkable results analysing the overhead catenary system.…”

Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review

“…The work of Lamas et al also use PCA, albeit in a slightly modified form, to align the direction of the tracks along the x-axis. Alignment of sub-point clouds to the track is also done by [36], [59]. Of course, the aforementioned partitioning of the scene into regular-sized pieces is also a form of normalisation.…”

“…For example, Corongiu et al flatten the point cloud to a 2D grid by summing in the zdirection. Within this image masts will be visible as highintensity blobs, making it easy to locate them [36].…”

Semantic Segmentation of Terrestrial Laser Scans of Railway Catenary Arches: A Use Case Perspective

“…A semi-automated method was used to detect the location of the catenary arches within these chunks of data. This method follows a similar approach as described by Zhu et al [29] and Corongiu et al [30]. The method is based on the assumption that poles are represented by a dense volume in the z-direction.…”

“…[ 29 ] and Corongiu et al. [ 30 ]. The method is based on the assumption that poles are represented by a dense volume in the z-direction.…”