Qualitative fault tree for the analysis of slope stability loss in road infrastructure

TRA Lisbon 2022 Conference Proceedings Transport Research Arena (TRA Lisbon 2022),14th-17th November 2022, Lisboa, PortugalLandslides are natural hazards all over the world, being the loss of slope stability in transport infrastructures considered small-scale landslides causing economic and human losses. Although there are many examples in the literature for the general analysis of landslides susceptibility, little information is available to analyze slopes at a local scale. The objective of this work is to identify all the slopes in road infrastructure, select those located in landslide susceptibility areas and apply a qualitative fault tree, also defined in this work, to determine the factors which can cause the slope stability loss. The results obtained are useful for each critical slope.Agencia Estatal de Investigación | Ref. PID2019-108816RB-I00Agencia Estatal de Investigación | Ref. PRE2020-09622

Comparing Mobile and Aerial Laser Scanner point cloud data sets for automating the detection and delimitation procedure of safety-critical near-road slopes

An inappropriately maintained road cut-slope is likely to fail, resulting in landslides or falling rocks that compromise road safety. Thus, road managers need to know the location of dangerous slopes along the road in order to prevent these events from happening. In this article, we compare two different approaches for conducting the digitization of the road environment and the automatic detection and delimitation of road slopes: Mobile Laser Scanners (MLS) and Aerial Laser Scanners (ALS). The point clouds obtained using the first kind of devices are dense, rich in detail and generated from a ground perspective; the second type of scanners produce less dense clouds from a zenithal perspective. We explore what is the effect of the point cloud density and scanner point of view over the slope detection procedure. Two road segments from the Spanish A55 and A52 highways were used as study zones, and a total of 28.61 km were analyzed. Better detection and delimitation results were achieved when using the ALS data and its corresponding algorithm. It was observed that the higher point density and detail of the MLS clouds were not an advantage for the slope detection task, and that measuring the road from a terrestrial perspective affected in a negative way during the detection process: the crest of the slopes often remained unmeasured, hidden behind vegetation or man-made elements, thus resulting in the slopes not being complete in the MLS clouds. Meanwhile, the whole slope structure is scanned when the scene is measured from an aerial perspective, henceforth obtaining better detection rates despite the relatively low resolution. The findings of this study provide valuable information in the field of road asset management, and help road managers make decisions when choosing what technology to use for the data gathering process.Agencia Estatal de Investigación | Ref. PID2022-140662OB-I00Universidade de Vigo/CISU

Heuristic generation of multispectral labeled point cloud datasets for deep learning models

Abstract. Deep Learning (DL) models need big enough datasets for training, especially those that deal with point clouds. Artificial generation of these datasets can complement the real ones by improving the learning rate of DL architectures. Also, Light Detection and Ranging (LiDAR) scanners can be studied by comparing its performing with artificial point clouds. A methodology for simulate LiDAR-based artificial point clouds is presented in this work in order to get train datasets already labelled for DL models. In addition to the geometry design, a spectral simulation will be also performed so that all points in each cloud will have its 3 dimensional coordinates (x, y, z), a label designing which category it belongs to (vegetation, traffic sign, road pavement, …) and an intensity estimator based on physical properties as reflectance.Ministerio de Ciencia, Innovación y Universidades | Ref. PCI2020-120705-

Santiago urban dataset SUD: Combination of Handheld and Mobile Laser Scanning point clouds

Santiago Urban Dataset SUD is a real dataset that combines Mobile Laser Scanning (MLS) and Handheld Mobile Laser Scanning (HMLS) point clouds. The data is composed by 2 km of streets, sited in Santiago de Compostela (Spain). Point clouds undergo a manual labelling process supported by both heuristic and Deep Learning methods, resulting in the classification of eight specific classes: road, sidewalk, curb, buildings, vehicles, vegetation, poles, and others. Three PointNet++ models were trained; the first one using MLS point clouds, the second one with HMLS point clouds and the third one with both H&MLS point clouds. In order to ascertain the quality and efficacy of each Deep Learning model, various metrics were employed, including confusion matrices, precision, recall, F1-score, and IoU. The results are consistent with other state-of-the-art works and indicate that SUD is valid for comparing point cloud semantic segmentation works. Furthermore, the survey's extensive coverage and the limited occlusions indicate the potential utility of SUD in urban mobility research.Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Universidade de Vigo/CISU