Integration of satellite monitoring data in a digital twin of transport infrastructure

In recent years, considerable attention has been directed towards the management of transportation infrastructure by stakeholders and governing bodies, aimed at adopting more effective and efficient technologies for monitoring and maintenance purposes. A comprehensive analysis of the health conditions of transportation assets and their surrounding environment plays a decisive role in identifying the factors influencing these infrastructures, including natural hazards and gradual morphological changes. Understanding environmental conditions, such as subsidence or landslide phenomena near transportation assets, is crucial for accurately predicting infrastructure variations over time and intervening promptly to prevent or efficiently repair damages. However, the monitoring of such phenomena (e.g., landslides) is generally not conducted at the network-scale level to evaluate the effects and potential risks on infrastructure assets. In this context, satellite remote sensing techniques, particularly utilizing multi-source and multi-frequency satellite missions, are increasingly applied for their capability to regularly monitor transport assets and extensive areas near their locations. This research explores the integration of information derived from satellite data, proposing a novel methodology for managing multi-sensor survey data by integrating satellite remote sensing into a dynamic Digital Twin of an infrastructure. To achieve this, Synthetic Aperture Radar (SAR) data were processed using Multi-Temporal SAR Interferometry (MTInSAR) to detect potential damages and changes in transport infrastructures and their surrounding environment. An experimental application focusing on a bridge was developed for this purpose. Data obtained from satellite remote sensing missions, including Sentinel-1 and COSMO-SkyMed missions, were integrated into the Digital Twin. A digital modeling process was developed to merge these datasets, resulting in the creation of a comprehensive Digital Twin representing both the bridge and its surrounding environment. This study establishes the foundation for an enhanced integrated monitoring methodology for critical transport infrastructures, emphasizing the importance of incorporating satellite monitoring data into Digital Twin frameworks to enhance infrastructure management and resilience.