Automated detection and decomposition of railway tunnels from Mobile Laser Scanning Datasets

Since the mid-19th century, the railway network has occupied a crucial place at the heart of the world's transport systems. Its infrastructure is often situated in harsh environments where an extreme event, or even daily use, could lead to a catastrophic accident. This is one of the main reasons why inspecting these constructions is so important. Despite the advances in this field, the human component continues to be part of the final inspection process. In order to improve on this, this paper shows the use of laser scanning as a leading technology in automating the inspection of railway infrastructures. The proposed methodologies provide the essential processed and classified data needed for the structural health monitoring of the various assets related to railways. It is divided into three main parts, which pre-process the point cloud, divide the cloud into ground and non-ground points, and detect the elements present in each of these clouds. The methods are validated in three case studies, each containing different railway tunnels. The results demonstrate that laser scanning technology, together with customized processing tools, can provide data for further structural operations with no requirement for either training in geomatics or high-performance computers for the data processing. Significant results are obtained for the developed classification methods: the classification of the tunnel elements returns a global F-Score of between 71 and 99% in a point-by-point comparison. With regard to the labelled rails classification, a global F-Score of 100% is achieved for the analyzed datasets, and between 56 and 73% for the point-by-point classification. •Automatic classification of large point clouds in tunnels' environments•Extraction of geometrical characteristics•Classification of lining, power lines, ground cantilever arms and rails•Point-by-point classification: global F-Score between 56 and 99%•Possible rails' classification (labels): global F-Score of 100%