The growing development of data digitalisation methods has increased their demand and applications in the transportation infrastructure field. Currently, mobile mapping systems (MMSs) are one of the most popular technologies for the acquisition of infrastructure data, with three-dimensional (3D) point clouds as their main product. In this work, a heuristic-based workflow for semantic segmentation of complex railway environments is presented, in which their most relevant elements are classified, namely, rails, masts, wiring, droppers, traffic lights, and signals. This method takes advantage of existing methodologies in the field for point cloud processing and segmentation, taking into account the geometry and spatial context of each classified element in the railway environment. This method is applied to a 90-kilometre-long railway lane and validated against a manual reference on random sections of the case study data. The results are presented and discussed at the object level, differentiating the type of the element. The indicators F1 scores obtained for each element are superior to 85%, being higher than 99% in rails, the most significant element of the infrastructure. These metrics showcase the quality of the algorithm, which proves that this method is efficient for the classification of long and variable railway sections, and for the assisted labelling of point cloud data for future applications based on training supervised learning models.

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Abstract. The growing trend of developing standards of information exchange and management processes is leading to Building Information Models (BIM) being adapted to work with linear infrastructure assets. For this reason, the Industry Foundation Classes (IFC) has developed standards for linear infrastructure such as roads. Furthermore, the usage of remote sensing technologies, such as Mobile Laser Scanning (MLS) systems for infrastructure monitoring is increasingly common. This paper presents an automated methodology that takes as input 3D point cloud tiles from an MLS and its trajectory, and outputs an IFC-compliant file that models the alignment of the road and the width of the roadway along the length of the road. The methodology is evaluated in 48 km of mountain roads, in some cases without road markings, using neither intensity nor colour fields.

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