2021-04-122020-03-202020-01-18036013231873684XWOS;000550146500008SCOPUS;2-s2.0-85084182110http://hdl.handle.net/10784/29129An 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 LtdengPERGAMON-ELSEVIER SCIENCE LTDBuildingsConvolutional neural networksCost effectivenessDisastersLearning systemsLossesRisk assessmentAutomatic DetectionBuilding populationBuilding typologiesFine-grained materialLateral load resisting systemsSeismic risk assessmentStructural typologiesTime-consuming tasksDeep learningartificial neural networkautomationconstruction materialdata setnatural disasterurban areainfo:eu-repo/semantics/article2020-03-102021-04-12Duque, J.Gonzalez, D.Rueda Plata, DiegoAcevedo, A.Ramos, R.Betancourt, A.García, S.10.1016/j.buildenv.2020.106805