Automatic detection of building typology using deep learning methods on street level images

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dc.citation.journalTitleBuilding and Environment
dc.contributor.authorDuque, J.
dc.contributor.authorGonzalez, D.
dc.contributor.authorRueda Plata, Diego
dc.contributor.authorAcevedo, A.
dc.contributor.authorRamos, R.
dc.contributor.authorBetancourt, A.
dc.contributor.authorGarcía, S.
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería de Producciónspa
dc.contributor.researchgroupMateriales de Ingenieríaspa
dc.creatorDuque, J.
dc.creatorGonzalez, D.
dc.creatorRueda Plata, Diego
dc.creatorAcevedo, A.
dc.creatorRamos, R.
dc.creatorBetancourt, A.
dc.creatorGarcía, S.
dc.date.accessioned2020-03-10
dc.date.accessioned2021-04-12T21:26:45Z
dc.date.available2021-04-12T21:26:45Z
dc.date.issued2020-03-20
dc.date.submitted2020-01-18
dc.description.abstractAn exposure model is a key component for assessing potential human and economic losses from natural disasters. An exposure model consists of a spatially disaggregated description of the infrastructure and population of a region under study. Depending on the size of the settlement area, developing such models can be a costly and time-consuming task. In this paper we use a manually annotated dataset consisting of approximately 10,000 photos acquired at street level in the urban area of Medellín to explore the potential for using a convolutional neural network (CNN) to automatically detect building materials and types of lateral-load resisting systems, which are attributes that define a building's structural typology (which is a key issue in exposure models for seismic risk assessment). The results of the developed model achieved a precision of 93% and a recall of 95% when identifying nonductile buildings, which are the buildings most likely to be damaged in an earthquake. Identifying fine-grained material typology is more difficult, because many visual clues are physically hidden, but our model matches expert level performances, achieving a recall of 85% and accuracy scores ranging from 60% to 82% on the three most common building typologies, which account for 91% of the total building population in Medellín. Overall, this study shows that a CNN can make a substantial contribution to developing cost-effective exposure models. © 2020 Elsevier Ltdeng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=11808
dc.identifier.doi10.1016/j.buildenv.2020.106805
dc.identifier.issn03601323
dc.identifier.issn1873684X
dc.identifier.otherWOS;000550146500008
dc.identifier.otherSCOPUS;2-s2.0-85084182110
dc.identifier.urihttp://hdl.handle.net/10784/29129
dc.language.isoeng
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.urihttps://www.sciencedirect.com/science/article/abs/pii/S0360132320301633
dc.rightsPERGAMON-ELSEVIER SCIENCE LTD
dc.sourceBuilding and Environment
dc.subjectBuildingseng
dc.subjectConvolutional neural networkseng
dc.subjectCost effectivenesseng
dc.subjectDisasterseng
dc.subjectLearning systemseng
dc.subjectLosseseng
dc.subjectRisk assessmenteng
dc.subjectAutomatic Detectioneng
dc.subjectBuilding populationeng
dc.subjectBuilding typologieseng
dc.subjectFine-grained materialeng
dc.subjectLateral load resisting systemseng
dc.subjectSeismic risk assessmenteng
dc.subjectStructural typologieseng
dc.subjectTime-consuming taskseng
dc.subjectDeep learningeng
dc.subjectartificial neural networkeng
dc.subjectautomationeng
dc.subjectconstruction materialeng
dc.subjectdata seteng
dc.subjectnatural disastereng
dc.subjecturban areaeng
dc.titleAutomatic detection of building typology using deep learning methods on street level imageseng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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