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Over the last few years, the management of multi-asset infrastructure over wide regions has experienced a significant evolution due to novel technologies or accessibility to cheaper sensors combined with the Internet of Things (IoT). Interferometric Synthetic Aperture Radar (InSAR) data has proven to be the most cost-effective technology to meet users’ requests. However, InSAR’s large volume of data presents challenges for end-users. Data aggregation and visualisation are needed to streamline operations and information transfer between teams. This proposed methodology seeks to address the challenges associated with InSAR data by integrating InSAR observations with the digital equivalent of existing structures in a repeatable and scalable manner. To achieve this, deformation profiles are generated adaptively, considering the asset’s characteristics, such as width and orientation, as well as the satellite acquisition geometry. In parallel, a two-dimensional approach is followed to associate InSAR information with each structure element and thus organise the information spatially. It should be noted that, even when using high-resolution radar data, it is sometimes challenging to associate radar returns with specific asset segments due to the complex interaction of microwaves with particular targets. Consequently, a key feature of this methodology is to classify InSAR data according to a precise geocoding of each scatterer, including an accurate estimation of its elevation. This methodology makes it possible to analyse many different types of infrastructure, which can be time-consuming for large highway or railway operators. The coverage of the InSAR results on a regional scale, and the semi-automated method gives decision-makers an overview of their structures to send maintenance teams, saving time and costs. This methodology allows for InSAR-derived product analysis, making the results more readable, accessible, and impactful. This type of infrastructure monitoring reflects a new maturity of InSAR in this field, demonstrated in real case studies.
Henrion et al. (Sat,) studied this question.