Road maintenance remains one of the most resource-intensive and hazardous operations in infrastructure management. Traditional inspection practices rely heavily on manual labour and discrete procedures, often resulting in limited scalability, operator exposure to traffic hazards, and inefficiencies in data collection. This paper presents a novel automated methodology that integrates Unmanned Aerial Vehicles (UAVs) and autonomous mobile robots (AMRs) to enable automated inspection and measurement of road assets through a digital twin (DT) system. The system leverages data fusion and real-time synchronisation between field agents and a centralised digital twin to monitor the retro-reflectivity of vertical and horizontal signage, detect obstacles and vegetation, and support data-driven maintenance planning. A case study conducted on the Italian highway network demonstrated improvements in operational safety, inspection efficiency, and measurement consistency. The results confirm that the integration of UAVs and AMRs within a digital twin framework can significantly improve sustainability, productivity, and workers’ safety in road maintenance operations.
Villaverde et al. (2026) studied this question.