Deep Learning for Building Attribute Classification from Street-View Images for Seismic Exposure Modeling

Exposure models are essential for seismic risk assessment to determine environmental vulnerabilities during earthquakes. However, developing these models at scale is challenging because it relies on manual inspection of buildings, which increases costs and introduces significant delays. Developing fast, consistent, and easy-to-deploy automated methods to support this process has become a priority. In this study, we investigate the use of deep learning to accelerate the classification of architectural and structural attributes from street-view imagery. Using the Alvalade dataset, which contains 4007 buildings annotated with 10 multi-class attributes, we evaluated the performance of multiple architecture types. Our analysis shows that deep learning models can successfully extract key structural features, achieving an average macro accuracy of 57%, and a Precision, Recall, and F1-score of 61%, 57%, and 56%, respectively. We also show that prediction quality is further improved by leveraging multi-view imagery of the target buildings. These results demonstrate that deep learning can be an effective solution to reduce the manual effort required for the development of reliable large-scale exposure models, offering a practical solution toward more efficient seismic risk assessment.