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.
Highlights
IntroductionMaintenance and monitoring of railway systems at national level is clearly a quite challenging task: rail lines are usually thousands of kilometers long, and several objects of interest are distributed all along such lines
Thanks to its safeness, railway transportation is still one of the most used public transportation methods for short to medium length travels, whereas long travels, such as intercontinental, are usually made by means of international flights.Maintenance and monitoring of railway systems at national level is clearly a quite challenging task: rail lines are usually thousands of kilometers long, and several objects of interest are distributed all along such lines.Despite several train companies operate in the Italian market, the physical Italian railway system is managed by the Italian Railway Network Enterprise (RFI), which is in charge of the maintenance and monitoring operations
MUIF is based on a geospatial database of several objects related to the railway system, such as switches, masts, cantilevers
Summary
Maintenance and monitoring of railway systems at national level is clearly a quite challenging task: rail lines are usually thousands of kilometers long, and several objects of interest are distributed all along such lines. Despite several train companies operate in the Italian market, the physical Italian railway system is managed by the Italian Railway Network Enterprise (RFI), which is in charge of the maintenance and monitoring operations. The acquisition of the geospatial data to be used to populate such geospatial database is clearly a not so easy task: given the huge rail line length and the complexity of the areas of interest, in particular on train stations, data acquisition is carried out by using different approaches and sensors:
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