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.researchgroupMecánica Aplicadaspa
dc.date.accessioned2020-03-10
dc.date.accessioned2021-04-16T20:10:43Z
dc.date.available2021-04-16T20:10:43Z
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/10784/29226
dc.language.isoengeng
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.publisher.departmentUniversidad EAFIT. Departamento de Ingeniería Mecánicaspa
dc.relation.urihttps://www.sciencedirect.com/science/article/abs/pii/S0360132320301633
dc.rightsPERGAMON-ELSEVIER SCIENCE LTD
dc.sourceBuilding and Environment
dc.subject.keywordBuildingseng
dc.subject.keywordConvolutional neural networkseng
dc.subject.keywordCost effectivenesseng
dc.subject.keywordDisasterseng
dc.subject.keywordLearning systemseng
dc.subject.keywordLosseseng
dc.subject.keywordRisk assessmenteng
dc.subject.keywordAutomatic Detectioneng
dc.subject.keywordBuilding populationeng
dc.subject.keywordBuilding typologieseng
dc.subject.keywordFine-grained materialeng
dc.subject.keywordLateral load resisting systemseng
dc.subject.keywordSeismic risk assessmenteng
dc.subject.keywordStructural typologieseng
dc.subject.keywordTime-consuming taskseng
dc.subject.keywordDeep learningeng
dc.subject.keywordartificial neural networkeng
dc.subject.keywordautomationeng
dc.subject.keywordconstruction materialeng
dc.subject.keyworddata seteng
dc.subject.keywordnatural disastereng
dc.subject.keywordurban 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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