dc.contributor.author | Rodriguez Antuñano, Ignacio | |
dc.contributor.author | Martínez Sánchez, Joaquín | |
dc.contributor.author | Cabaleiro Núñez, Manuel | |
dc.contributor.author | Riveiro Rodríguez, Belén | |
dc.date.accessioned | 2023-08-31T08:15:02Z | |
dc.date.available | 2023-08-31T08:15:02Z | |
dc.date.issued | 2023-08-04 | |
dc.identifier.citation | Remote Sensing, 15(15): 3867 (2023) | spa |
dc.identifier.issn | 20724292 | |
dc.identifier.uri | http://hdl.handle.net/11093/5105 | |
dc.description.abstract | Large-scale infrastructure monitoring and vulnerability assessment are crucial for the preservation and maintenance of built environments. To ensure the safety of urban infrastructure against natural and man-made disasters, constant monitoring is crucial. To do so, satellite Earth observation (EO) is being proposed, particularly radar-based imaging, because it allows large-scale constant monitoring since radar signals are not blocked by clouds and can be collected during both day and night. The proposed methodology for large-scale infrastructure monitoring and vulnerability assessment is based on MT-InSAR time series analysis. The homogeneity of the year-to-year displacement trend between each point and its surrounding points is evaluated to determine whether the area is a stable or vulnerable zone. To validate the methodology, four case studies of recently collapsed infrastructures are analyzed. The results indicate the potential of the proposed methodology for predicting and preventing structural collapses. | en |
dc.description.sponsorship | Ministerio de Ciencia e Innovación | Ref. PID2021-124236OB-C33 | spa |
dc.language.iso | eng | spa |
dc.publisher | Remote Sensing | spa |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2021-124236OB-C33/ES | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | Anticipating the collapse of urban infrastructure: a methodology based on Earth Observation and MT-InSAR | en |
dc.type | article | spa |
dc.rights.accessRights | openAccess | spa |
dc.relation.projectID | info:eu-repo/grantAgreement/EU/H2020/958171 | |
dc.identifier.doi | 10.3390/rs15153867 | |
dc.identifier.editor | https://www.mdpi.com/2072-4292/15/15/3867 | spa |
dc.publisher.departamento | Enxeñaría dos recursos naturais e medio ambiente | spa |
dc.publisher.departamento | Enxeñaría dos materiais, mecánica aplicada e construción | spa |
dc.publisher.grupoinvestigacion | Xeotecnoloxías Aplicadas | spa |
dc.subject.unesco | 3311.02 Ingeniería de Control | spa |
dc.subject.unesco | 3305.06 Ingeniería Civil | spa |
dc.date.updated | 2023-08-31T08:10:39Z | |
dc.computerCitation | pub_title=Remote Sensing|volume=15|journal_number=15|start_pag=3867|end_pag= | spa |