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A platform for research: civil engineering, architecture and urbanism
Automatic detection of building typology using deep learning methods on street level images
https://orcid.org/0000-0003-2818-3323
https://orcid.org/0000-0002-3869-4373
https://orcid.org/0000-0002-4016-681X
Abstract An 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.
Highlights We use a convolutional neural network to detect building typologies. Our model is a major contribution to developing cost-effective exposure models. The model has a precision and recall above 93% when detecting nonductile buildings. This paper uses a dataset of about 10,000 photos manually annotated by experts.
Automatic detection of building typology using deep learning methods on street level images
https://orcid.org/0000-0003-2818-3323
https://orcid.org/0000-0002-3869-4373
https://orcid.org/0000-0002-4016-681X
Abstract An 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.
Highlights We use a convolutional neural network to detect building typologies. Our model is a major contribution to developing cost-effective exposure models. The model has a precision and recall above 93% when detecting nonductile buildings. This paper uses a dataset of about 10,000 photos manually annotated by experts.
Automatic detection of building typology using deep learning methods on street level images
https://orcid.org/0000-0003-2818-3323
https://orcid.org/0000-0002-3869-4373
https://orcid.org/0000-0002-4016-681X
Abstract An 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.
Highlights We use a convolutional neural network to detect building typologies. Our model is a major contribution to developing cost-effective exposure models. The model has a precision and recall above 93% when detecting nonductile buildings. This paper uses a dataset of about 10,000 photos manually annotated by experts.
Automatic detection of building typology using deep learning methods on street level images
Gonzalez, Daniela (author) / Rueda-Plata, Diego (author) / Acevedo, Ana B. (author) / Duque, Juan C. (author) / Ramos-Pollán, Raúl (author) / Betancourt, Alejandro (author) / García, Sebastian (author)
Building and Environment ; 177
2020-03-10
Article (Journal)
Electronic Resource
English
Coding for predictive built environments: building and street typology choices in form-based codes
| Taylor & Francis Verlag | 2024