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dc.contributor.authorRodriguez Antuñano, Ignacio 
dc.contributor.authorMartínez Sánchez, Joaquín 
dc.contributor.authorCabaleiro Núñez, Manuel 
dc.contributor.authorRiveiro Rodríguez, Belén 
dc.date.accessioned2023-08-31T08:15:02Z
dc.date.available2023-08-31T08:15:02Z
dc.date.issued2023-08-04
dc.identifier.citationRemote Sensing, 15(15): 3867 (2023)spa
dc.identifier.issn20724292
dc.identifier.urihttp://hdl.handle.net/11093/5105
dc.description.abstractLarge-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.sponsorshipMinisterio de Ciencia e Innovación | Ref. PID2021-124236OB-C33spa
dc.language.isoengspa
dc.publisherRemote Sensingspa
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2021-124236OB-C33/ES
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleAnticipating the collapse of urban infrastructure: a methodology based on Earth Observation and MT-InSARen
dc.typearticlespa
dc.rights.accessRightsopenAccessspa
dc.relation.projectIDinfo:eu-repo/grantAgreement/EU/H2020/958171
dc.identifier.doi10.3390/rs15153867
dc.identifier.editorhttps://www.mdpi.com/2072-4292/15/15/3867spa
dc.publisher.departamentoEnxeñaría dos recursos naturais e medio ambientespa
dc.publisher.departamentoEnxeñaría dos materiais, mecánica aplicada e construciónspa
dc.publisher.grupoinvestigacionXeotecnoloxías Aplicadasspa
dc.subject.unesco3311.02 Ingeniería de Controlspa
dc.subject.unesco3305.06 Ingeniería Civilspa
dc.date.updated2023-08-31T08:10:39Z
dc.computerCitationpub_title=Remote Sensing|volume=15|journal_number=15|start_pag=3867|end_pag=spa


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    Attribution 4.0 International
    Except where otherwise noted, this item's license is described as Attribution 4.0 International