RT Journal Article
T1 Automatic point cloud semantic segmentation of complex railway environments
A1 Lamas Novoa, Daniel
A1 Soilán Rodríguez, Mario
A1 Grandio Gonzalez, Javier
A1 Riveiro Rodríguez, Belén
K1 3305.06 Ingeniería Civil
K1 3310.04 Ingeniería de Mantenimiento
K1 3323.02 Equipo Ferroviario
AB 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.
PB Remote Sensing
SN 20724292
YR 2021
FD 2021-06-14
LK http://hdl.handle.net/11093/2347
UL http://hdl.handle.net/11093/2347
LA eng
NO Remote Sensing, 13(12): 2332 (2021)
NO Agencia Estatal de Investigación | Ref. RTI2018-095893-B-C21
DS Investigo
RD 03-dic-2024