## 5 Satellite Remote Sensing Techniques for Infrastructure Monitoring
### Stand-Alone Applications of Satellite Remote Sensing for Infrastructure Monitoring
#### 5.3.4 Railways
A dataset of X-band data, acquired by ASI in the framework of the COSMO-SkyMed mission, was processed for the monitoring of the Rochester Bridge, UK. In [130], a clustering technique based on the data semantic of the PSs observed in the bridge area allowed the allocation of relevant information to individual structural elements of the bridge, such as piers and arcs. To quantitatively evaluate the PSI results, the profile of the spatial distributions of the average deformation velocity (mm/yr) related to the PSs on the road bridge along the longitudinal axis was investigated [130]. Overall, a range of variation of a few millimetres for the observed displacements was noticed across maximum and minimum values. No critical areas or significant subsidence were detected for the relevant PS data clusters. The aforementioned studies clearly show a growing interest in the application of integrated approaches to assess the health and internal condition of transport structures, paving the way for further investigations and research studies. Advances, Benefits and Challenges for Network-Level Infrastructure Monitoring with Satellite Remote Sensing and NDTs  
It is well recognised that decay and ageing exacerbate the costs of maintenance. Asset owners allocate considerable funding to road infrastructure, rail maintenance and repairing operations to ensure structural integrity and proper serviceability of the network system. It is therefore important to monitor the infrastructure and its individual structural elements (e.g., superstructures, piers, decks) with effective solutions and technologies. This allows the maintenance of high standards for the structures' mechanical responses, resistance to fatigue loads and resistance to geohazard events (e.g., earthquakes and sudden subsidence), all of which can affect the integrity of infrastructure assets. The main concept of an integrated health monitoring model is reported in Figure 14. This is representative of a synergistic analysis of outputs from satellite remote sensing and NDT technologies. This integrated approach relies on information available from asset owners' databases, made up of geographical asset location, as-built information of identified infrastructure network elements. The main novelties are related to the implementation of data from historical monitoring and maintenance interventions, which can be used as a starting point to reconstruct historical series from the monitoring of the target infrastructure. Accordingly, the model is characterised by two synchronised routine monitoring stages by two levels of data coverage, i.e., the local level for the use of NDTs and the network level for the implementation of satellite remote sensing techniques. Where critical sections are identified, local inspections can be carried out with dedicated NDT techniques. This is intended to build up a more comprehensive information system on the type and scale of the developing distress at the identified sections. The information obtained at this stage forms the basis of prediction models for the evaluation of stresses and anomalies. These models can support assessment of whether maintenance or rehabilitation is required and of the priority level to be assigned to the identified intervention. Sections not labelled with any critical occurrence are subject to new screening phases at predetermined time intervals. The main aim of this phase is to assess any potential distress in the infrastructure in terms of the extent of the degradation (e.g., portion of the asset affected by low bearing capacity of the subgrades), causes (e.g., the presence of fine materials in the subbase courses) and effects (e.g., low-stiffness sections observed by deflection tests) that cannot be detected by satellite remote sensing. The sources of damage can be effectively identified by GPR as potential causes for the displacements detected by MT-InSAR.