Earth observation and digitalization for critical civil Infrastructures: The Digital Twin 2.0

The increasing ageing of transport infrastructure networks, combined with growing exposure to environmental and climate-related hazards, requires new monitoring approaches capable of integrating information from multiple spatial and temporal scales. Traditional inspection methods and local non-destructive testing (NDT) techniques provide highly accurate information at the structural level but are limited in terms of spatial coverage and temporal continuity. Conversely, Earth Observation (EO) technologies, particularly satellite-based Interferometric Synthetic Aperture Radar (InSAR), enable systematic and large-scale monitoring. This research addresses the fragmentation between local-scale investigations and territorialscale observations by proposing a new methodological framework referred to as Digital Twin 2.0. The proposed paradigm aims to integrate multi-source monitoring data. The methodology introduces procedures for multi-source data harmonization, including upscaling and downscaling techniques that allow datasets characterized by different spatial resolutions and information densities to be combined within a consistent analytical framework. The integrated datasets are then incorporated into parametric digital models, enabling the creation of dynamic Digital Twins capable of linking heterogeneous monitoring data with specific structural components of the infrastructure. The proposed framework is validated through different case studies. These applications demonstrate the capability of the Digital Twin 2.0 approach to support multi-scale monitoring, enhance the interpretation of satellite-derived deformation measurements, and provide a comprehensive digital environment for infrastructure diagnostics, risk assessment, and proactive maintenance planning. The results highlight the potential of integrating Earth Observation technologies with digital modeling and in situ investigations, paving the way for next-generation infrastructure monitoring frameworks based on dynamic and multi-source Digital Twins.