High resolution 3D data for Pavement Condition Assessment in a Digital Twin perspective

The use of Mobile Mapping Systems (MMS) has revolutionized urban road infrastructure management, offering unprecedentedprecision and efficiency in data acquisition and analysis. This study focuses on the application of the RIEGL VMY-2 MMS to assesspavement conditions in an urban environment. The RIEGL VMY-2 system, equipped with dual LiDAR sensors and sphericalcameras, enabled the collection of high-density point clouds enriched with RGB and intensity values. These attributes were criticalfor the automated detection and characterization of pavement defects, such as cracks, potholes, and deformations. Advancedalgorithms processed the MMS data to classify the point cloud, extract surface features, and attribute semantic information, such asdefect severity and location. Additionally, the study integrates Building Information Modeling (BIM) methodologies to enhanceurban infrastructure management. By incorporating the processed geospatial data into a BIM environment, municipalities can createcomprehensive digital representations of road assets, facilitating improved planning, maintenance, and lifecycle management. TheBIM model serves as a dynamic repository that links geometric and semantic data, offering a more structured and interactiveapproach to infrastructure monitoring. The results demonstrate the potential of MMS technologies in creating actionable geospatialdatasets for urban infrastructure management. The geospatial database generated through this workflow includes detailed pavementcondition maps and the Pavement Condition Index (PCI), enabling municipalities to prioritize maintenance interventions andoptimize resource allocation. This study underscores the critical role of MMS technologies in modernizing urban infrastructuremanagement, bridging the gap between raw geospatial data and actionable insights for sustainable urban planning.