Digital twinning of railway overhead line equipment from airborne lidar data

The automated generation of geometry-only digital twins of Overhead Line Equipment (OLE) system in existing railways from point clouds is an unsolved problem. Currently, this process is highly reliant upon manual inputs, needing 10 times more labour hours than scanning the physical asset. The resulting modelling cost counteracts the expected benefits of the digital twin. We tackle this challenge using a novel model-driven method that exploits the highly regulated and standardised nature of railways. It starts by restricting the search for OLE elements relative to point clusters of the railway masts. The resulting point clusters of the OLE elements are then converged with various parametric models of different catenary configurations to verify the presence of OLE elements and to find the best possible fit. The method outputs a geometry-only digital twin of the OLE system in Industry Foundation Classes (IFC) format. The method was tested on an 18 km railway point cloud and achieves overall detection rates of 93.2% F1 score for OLE cables and 98.1% F1 score for other OLE elements. The accuracy of the generated model is evaluated using distance-based metrics between the ground truth model and the automated model. The average modelling distance is 3.82 cm Root Mean Square Error (RMSE) for all 18 km dataCambridge Commonwealth, European & International Trust Bentley Systems UK Plc

Classification of high-voltage power line structures in low density ALS data acquired over broad non-urban areas

Airborne laser scanning (ALS) has gained importance over recent decades for multiple uses related to the cartography of landscapes. Processing ALS data over large areas for forest resource estimation and ecological assessments requires efficient algorithms to filter out some points from the raw data and remove human-made structures that would otherwise be mistaken for natural objects. In this paper, we describe an algorithm developed for the segmentation and cleaning of electrical network facilities in low density (2.5 to 13 points/m2) ALS point clouds. The algorithm was designed to identify transmission towers, conductor wires and earth wires from high-voltage power lines in natural landscapes. The method is based on two priors i.e. (1) the availability of a map of the high-voltage power lines across the area of interest and (2) knowledge of the type of transmission towers that hold the conductors along a given power line. It was tested on a network totalling 200 km of wires supported by 415 transmission towers with diverse topographies and topologies with an accuracy of 98.6%. This work will help further the automated detection capacity of power line structures, which had previously been limited to high density point clouds in small, urbanised areas. The method is open-source and available online

A deep learning approach towards railway safety risk assessment

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks