While a geo-hazard risk assessment of bridges is crucial for achieving the United Nations’ Sustainable Development Goals, state-of-the-art methods for evaluation of risk neglect the temporal dimension of structural vulnerability, overlooking how monitoring systems like Structural Health Monitoring sensors and Multi-Temporal Interferometric Synthetic Aperture Radar can continuously track bridge conditions. Moreover, despite Structural Health Monitoring systems being sparsely installed, no research has quantified the global potential of this spaceborne radar-based technique as a complementary monitoring solution for bridges. This study introduces a method that integrates monitoring availability into structural vulnerability assessments and evaluates the global risk of long-span bridges affected by subsidence and landslides. Findings revealed that while fewer than 20% of bridges have Structural Health Monitoring systems, spaceborne monitoring could provide monitoring for over 60% of structures, leveraging Sentinel-1’s global coverage. Incorporating this satellite remote sensing approach into routine assessments could decrease the number of bridges classified as high-risk by one-third. Moreover, half of the remaining high-risk structures could benefit from spaceborne monitoring, highlighting the technique’s potential to enhance structural safety and resilience, especially in economically disadvantaged regions.

Predicting the availability of measurement points provided by Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) poses a challenge due to a nonuniform distribution of Persistent Scatterers (PSs). This article introduces a novel method to estimate the availability of MT-InSAR results on buildings and infrastructure networks, eliminating the need for labor-intensive and time-consuming analyses of the entire SAR data stack. The method is based on an analysis of the interferometric coherence decay characteristics and data regarding buildings and transport infrastructure location as inputs to a convolutional neural network. Specifically, a U-Net architecture model was implemented and trained to predict the PS density of Sentinel-1 data. The methodology was applied to a regional-scale analysis of the Dutch infrastructure, resulting in a low 1.06pm0.10 mean absolute error in the pixel-based PS count estimation on the test data split, with over 80% of predictions within pm1 from the actual value. The model achieved high accuracy when applied to a previously unseen dataset, demonstrating strong generalization performance. The proposed workflow, with its notable ability to accurately predict areas lacking measurement points, offers stakeholders a tool to assess the feasibility of applying MT-InSAR for specific structures. Thereby, it enhances infrastructure reliability by addressing a critical need in decision-making processes and improving the applicability of MT-InSAR for structural health monitoring of infrastructure assets.