Hierarchical Clustering and Small Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) for Remotely Sensed Building Identification and Risk Prioritisation
The conventional Structural Health Monitoring (SHM) framework focuses on individual structures. However, preliminary studies are required at a large territorial scale to effectively identify the most vulnerable elements. This becomes particularly challenging in urban settings, where numerous buildin...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/128 |
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| author | Yassir Hamzaoui Marco Civera Andrea Miano Manuela Bonano Francesco Fabbrocino Andrea Prota Bernardino Chiaia |
| author_facet | Yassir Hamzaoui Marco Civera Andrea Miano Manuela Bonano Francesco Fabbrocino Andrea Prota Bernardino Chiaia |
| author_sort | Yassir Hamzaoui |
| collection | DOAJ |
| description | The conventional Structural Health Monitoring (SHM) framework focuses on individual structures. However, preliminary studies are required at a large territorial scale to effectively identify the most vulnerable elements. This becomes particularly challenging in urban settings, where numerous buildings of varied shapes, ages, and structural conditions are closely spaced from one another. A twofold task is therefore required: the automated identification and differentiation of various structures, coupled with a ranking system based on perceived structural risk, here assumed to be linked to their deformation patterns. It integrates displacement measurements acquired through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique, specifically employing the full-resolution Small Baseline Subset (SBAS) approach coupled with Hierarchical Clustering. The effectiveness of this method is successfully demonstrated and validated in two selected areas of Rome, Italy, serving as case studies. The results of this vast-area scale monitoring can be used to select the constructions that need a more in-depth assessment. |
| format | Article |
| id | doaj-art-09f1edd7087049f180a0b6c8430d9eac |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-09f1edd7087049f180a0b6c8430d9eac2025-01-10T13:20:20ZengMDPI AGRemote Sensing2072-42922025-01-0117112810.3390/rs17010128Hierarchical Clustering and Small Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) for Remotely Sensed Building Identification and Risk PrioritisationYassir Hamzaoui0Marco Civera1Andrea Miano2Manuela Bonano3Francesco Fabbrocino4Andrea Prota5Bernardino Chiaia6Department of Structural, Building and Geotechnical Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, ItalyDepartment of Structural, Building and Geotechnical Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, ItalyDepartment of Engineering, Telematic University Pegaso, Centro Direzionale ISOLA F2, 80143 Napoli, ItalyConsiglio Nazionale delle Ricerche (CNR)—Institute for Electromagnetic Sensing of the Environment, Via Diocleziano 328, 80124 Naples, ItalyDepartment of Engineering, Telematic University Pegaso, Centro Direzionale ISOLA F2, 80143 Napoli, ItalyDepartment of Structures for Engineering and Architecture, University of Naples “Federico II”, Via Claudio 21, 80125 Naples, ItalyDepartment of Structural, Building and Geotechnical Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, ItalyThe conventional Structural Health Monitoring (SHM) framework focuses on individual structures. However, preliminary studies are required at a large territorial scale to effectively identify the most vulnerable elements. This becomes particularly challenging in urban settings, where numerous buildings of varied shapes, ages, and structural conditions are closely spaced from one another. A twofold task is therefore required: the automated identification and differentiation of various structures, coupled with a ranking system based on perceived structural risk, here assumed to be linked to their deformation patterns. It integrates displacement measurements acquired through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique, specifically employing the full-resolution Small Baseline Subset (SBAS) approach coupled with Hierarchical Clustering. The effectiveness of this method is successfully demonstrated and validated in two selected areas of Rome, Italy, serving as case studies. The results of this vast-area scale monitoring can be used to select the constructions that need a more in-depth assessment.https://www.mdpi.com/2072-4292/17/1/128machine learningHierarchical ClusteringDifferential Interferometric Synthetic Aperture Radar (DInSAR)Small Baseline Subset (SBAS)COSMO-SkyMedQGIS |
| spellingShingle | Yassir Hamzaoui Marco Civera Andrea Miano Manuela Bonano Francesco Fabbrocino Andrea Prota Bernardino Chiaia Hierarchical Clustering and Small Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) for Remotely Sensed Building Identification and Risk Prioritisation Remote Sensing machine learning Hierarchical Clustering Differential Interferometric Synthetic Aperture Radar (DInSAR) Small Baseline Subset (SBAS) COSMO-SkyMed QGIS |
| title | Hierarchical Clustering and Small Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) for Remotely Sensed Building Identification and Risk Prioritisation |
| title_full | Hierarchical Clustering and Small Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) for Remotely Sensed Building Identification and Risk Prioritisation |
| title_fullStr | Hierarchical Clustering and Small Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) for Remotely Sensed Building Identification and Risk Prioritisation |
| title_full_unstemmed | Hierarchical Clustering and Small Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) for Remotely Sensed Building Identification and Risk Prioritisation |
| title_short | Hierarchical Clustering and Small Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) for Remotely Sensed Building Identification and Risk Prioritisation |
| title_sort | hierarchical clustering and small baseline subset differential interferometric synthetic aperture radar sbas dinsar for remotely sensed building identification and risk prioritisation |
| topic | machine learning Hierarchical Clustering Differential Interferometric Synthetic Aperture Radar (DInSAR) Small Baseline Subset (SBAS) COSMO-SkyMed QGIS |
| url | https://www.mdpi.com/2072-4292/17/1/128 |
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