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

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