The goal of urban digitalization is to provide a digital representation of physical infrastructure, data, information, and procedures for the management of complex anthropogenic systems. To meet this goal, mobile surveying is a consolidated method for data collection in urban environments. The mobile mapping system (MMS), is a versatile tool used to collect geospatial data efficiently, accurately, and quickly, with reduced time and costs compared to traditional survey methods.

The aim of this study was to analyze the limitations, possible implementations, and the state of the art of MMSs for road infrastructure monitoring in order to create a DT (digital twin) for road infrastructure management, with a specific focus on extracting value-added information from a survey dataset. The case study presented here was part of the Turin Digital Twin project.

Modern urban analysis requires high-resolution spatial data, often at a millimeter scale, which traditional aerial datasets alone cannot provide. A terrestrial mobile mapping system survey was conducted using a RIEGL VMY-2 equipped with dual LiDAR sensors and a spherical camera (Teledyne FLIR, Wilsonville, OR, USA) to capture high-density 3D point clouds.

By extracting value-added information from high-resolution MMS datasets, this methodology supports several municipal functions, including the following:

• Pavement condition assessment: enabling predictive maintenance and optimized intervention planning.
• Urban asset inventory: mapping horizontal road markings, utility poles, traffic signs, and overhead structures to enhance road safety and regulatory compliance.
• Integration into a DT: structuring data into a dynamic model that supports real-time monitoring and decision making for urban planning.

Overall, the proposed methodology based on the high-resolution, highly informative data acquired with the RIEGL VMY-2 Mobile Mapping System underscores the transformative potential of integrating multisource datasets and advanced analytics for urban infrastructure management.

The full paper was published in March 2025 at www.researchgate.net and can be found here.