Transportation infrastructure strengthens societal and economic activities. However, the ageing of structures, compounded by environmental stressors and increasing usage, amplifies the demand for innovative, dynamic, and scalable monitoring solutions. Traditional methods, such as on-site inspections and static analyses, face limitations in providing consistent, real-time assessments over large-scale networks. To address these challenges, an integrated approach combining Digital Twin (DT) technology, advanced remote sensing, and Building Information Modeling (BIM) is proposed, enabling a transformative paradigm in infrastructure management. Digital Twins act as intelligent digital replicas of physical assets, capable of integrating real-time data from diverse monitoring technologies [1-2]. This study emphasizes the potential of combining Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data with insights from laser scanner surveys, thermographic analyses, and UAV-based inspections. Laser scanners capture precise geometrical details and deformation patterns, while thermography identifies material degradation and subsurface anomalies. UAVs provide high-resolution imaging and localized monitoring, enhancing the density of data acquisition. These complementary techniques are further augmented by satellite-based information, which provides millimetre displacement measurements across large areas with high temporal and spatial resolution. Central to this research is the concept of data fusion, wherein the integration of multi-source datasets—including satellite imagery, laser scanners, thermal analyses, and UAV surveys—enables a holistic understanding of infrastructure conditions. The fusion of these diverse data streams within BIM and DT frameworks can facilitate dynamic, near-real-time monitoring, predictive maintenance, and optimized decision-making processes. Focusing on critical transportation assets such as bridges and viaducts, this study highlights how these integrated technologies advance the detection of structural anomalies, assess ageing effects, and support lifecycle management. The findings underscore the transformative potential of combining high-resolution satellite constellations, UAV technologies, and BIM-based Digital Twins to create resilient and sustainable infrastructure monitoring systems. This approach offers significant scalability, precision, and efficiency, establishing a new standard for the continuous management and preservation of transportation infrastructure.

Acknowledgements

This research is supported by the Project “PIASTRE” accepted and funded by the Lazio Region, Italy.

References

[1] Gagliardi V., et al. Digital twin implementation by multisensors data for smart evaluation of transport infrastructure. SPIE Optical Metrology. Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, Munich, 2023.

[2] Napolitano A., et al., Integration of Satellite Monitoring data in a Digital Twin of Transport Infrastructure. Proceedings Volume 13197, Earth Resources and Environmental Remote Sensing/GIS Applications XV; 131970Y (2024) https://doi.org/10.1117/12.3034395