Road condition monitoring plays a critical role in transportation infrastructure maintenance and traffic safety assurance. This research introduces a methodology to detect cracks on pavement point clouds acquired with Mobile Laser Scanning systems, which offer more versatility and comprehensive information about the road environment than other specific surveying systems (i.e., profilometers, 3D cameras). The methodology comprises the following steps: (1) Road segmentation; (2) the detection of candidate crack points in individual scanning lines of the point cloud, based on point elevation; (3) crack point clustering via a region-growing algorithm; and (4) crack geometrical attributes extraction. Both the profile evaluation and the region-growing clustering algorithms have been developed from scratch to detect cracks directly from 3D point clouds instead of using raster data or Geo-Referenced Feature images, offering a quick and effective pre-rating tool for pavement condition assessment. Crack detection is validated with data from damaged roads in Portugal.

Sunlight conditions can reduce drivers’ visibility, which is a safety concern on road networks. This research introduces a method to study sun glare incidence in road environments. Sun glare areas during daylight hours are automatically detected from mobile laser scanning (MLS) and aerial laser scanning (ALS) point clouds. The method comprises the following steps. First, the Sun’s position (solar altitude and azimuth) referring to a location is calculated. Second, the incidence of sun glare with the user’s angle of vision is analyzed based on human vision. Third, sun ray intersections with near obstacles (vegetation, building, etc.) are calculated utilizing MLS point clouds. Finally, intersections with distant obstacles (mountains) are calculated utilizing ALS point clouds. MLS and ALS data are processed in order to combine both data types, remove outliers, and optimize computational time for intersection searches (point density reduction and Delaunay triangulation). The method was tested on two real case studies, covering roads with different bearings, slopes, and surroundings. The combination of MLS and ALS data, together with the solar geometry, identify areas of risk for the visibility of drivers. Consequently, the proposed method can be utilized to reduce sun glare, implementing warnings in navigation systems.

Although multi-temporal acquisitions are a widely accepted way to reduce occlusions in point clouds, occlusion analysis continues to be a visual analysis. The objective of this work is the design of an automatic method for the detection and characterization of street point cloud occlusions acquired with Mobile Laser Scanning (MLS). The proposed method consists of four main phases: alignment of the point cloud, rasterization, generation of occluded point clouds and visibility analysis. The proposed method was tested with point clouds of TerraMobil-ita/iQmulus dataset. The result shows a correct detection of 98.71% of the occlusions on ground and 83.37% on façades. All detected occlusions were correctly characterized. In the case studies, cars generated the largest amount of occluded area on the ground, with an occluded area per object of 15.36 m2. The main source of occlusions on façades was the building geometry and windows.