ANIDIS - L'ingegneria Sismica in Italia, ANIDIS XX - 2025

Dimensione del carattere:  Piccola  Media  Grande

Analysis of multi-source satellite-derived displacement data for structural monitoring of masonry heritage buildings

Stefania Coccimiglio, Raffaele Tarantini, Gaetano Miraglia, Irene Matteini, Linda Scussolini, Rosario Ceravolo, Giuseppe Andrea Ferro

Ultima modifica: 2025-08-06

Sommario


Historical masonry structures, such as churches and towers, represent a fundamental part of our cultural heritage but are particularly vulnerable due to both aging and the external influences they are subjected to. In this context, Structural Health Monitoring (SHM) plays a crucial role in assessing structural integrity and preventing damage. In recent years, satellite remote sensing data, such as those obtained through interferometric techniques (InSAR), have gained increasing relevance in SHM applications, also due to the free availability of pre-processed datasets from missions like Sentinel-1 (EGMS). However, the direct use of such data may often be limited, as they are provided in a pre-processed form, reducing the user ability to customize and adapt the analysis. This study presents a comparison between pre-processed EGMS data and data obtained through user-controlled processing using dedicated software, with the aim of evaluating the advantages and limitations of each approach. Using data obtained through user-controlled processing allows for the targeted selection of reliable points while discarding those considered inconsistent or of low quality, thus ensuring a more robust and tailored analysis. Additionally, a comparison is conducted between displacement data from Sentinel-1 and those from the COSMO-SkyMed constellation, in order to explore the informative potential of the two satellite systems. This processed data could be useful to calibrate a numerical model, enabling the joint optimization of mechanical parameters and external imposed actions (e.g., displacements), thus improving the prediction of structural behaviour and supporting more robust SHM models.


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