Automatic Generation of Urban Road 3D Models for Pedestrian Studies from LiDAR Data

Abstract

The point clouds acquired with a mobile LiDAR scanner (MLS) have high density and accuracy, which allows one to identify different elements of the road in them, as can be found in many scientific references, especially in the last decade. This study presents a methodology to characterize the urban space available for walking, by segmenting point clouds from data acquired with MLS and automatically generating impedance surfaces to be used in pedestrian accessibility studies. Common problems in the automatic segmentation of the LiDAR point cloud were corrected, achieving a very accurate segmentation of the points belonging to the ground. In addition, problems caused by occlusions caused mainly by parked vehicles and that prevent the availability of LiDAR points in spaces normally intended for pedestrian circulation, such as sidewalks, were solved in the proposed methodology. The innovation of this method lies, therefore, in the high definition of the generated 3D model of the pedestrian space to model pedestrian mobility, which allowed us to apply it in the search for shorter and safer pedestrian paths between the homes and schools of students in urban areas within the Big-Geomove project. Both the developed algorithms and the LiDAR data used are freely licensed for their use in further research.

Laser Scanning for Terrain Analysis and Route Design for Electrified Public Transport in Urban Areas

“…Three-dimensional data are the main source of information for the determination of the orography of the terrain. These data can be obtained from different sensors [22], among which LiDAR is the most used in several fields of study, such as water [23], forest [24], mobility [25], and energy studies [26]. Several pieces of research use LiDAR products to analyze terrain parameters.…”

Methodology for Identifying Optimal Pedestrian Paths in an Urban Environment: A Case Study of a School Environment in A Coruña, Spain

“…To obtain a digital terrain model (DTM) of the study area and the location of all obstacles hindering pedestrian movement, the algorithm proposed in a previous study (Fernández-Arango et al, 2022) [23] was employed. This algorithm allowed the generation of a DTM raster from LiDAR point clouds acquired through MLS.…”

“…Of the streets studied (Figure 1), all necessary pedestrian crossings were successfully identified to determine pedestrian routes. For validating the model inferences, the obtained segmentations were compared with a ground truth of the study region, which had previously been utilized in other works such as Fernández-Arango et al (2022) and Esmorís et al (2023) [23,35]. A total of 40 pedestrian crossings were identified, corresponding to 10 streets in the vicinity of Fogar de Sta.…”

Efficiency of Terrestrial Laser Scanning in Survey Works: Assessment, Modelling, and Monitoring

“…Ma et al [14] extract road points by utilising the deep learning network PointNet++(see Qi et al [15]), afterwards, the road points are processed based on graphcut and constrained Triangulation Irregular Networks (TIN), and both the commission and omission errors are decreased.Finally, collinearity and width similarity are suggested to approximate the linking probability of road segments. Fernández-Arango et al [16] propose an approach for pedestrian space extracting and generating a high-definition 3D model. They start by separating terrain and off-terrain classes, and then a K-distance filter is applied to improve and detect the pedestrian spaces.…”