Estimation of Fireproof Structure Class and Construction Year for Disaster Risk Assessment
This work addresses a practical issue for insurance and urban planning in Japan by enabling scalable risk profiling, though it is incremental as it applies existing methods to a new domain-specific dataset.
The study tackled the problem of missing building metadata for disaster risk assessment in Japan by developing a multi-task learning model that predicts construction year, building structure, and property type from facade images, achieving high accuracy in construction-year regression and robust classification across imbalanced categories.
Structural fireproof classification is vital for disaster risk assessment and insurance pricing in Japan. However, key building metadata such as construction year and structure type are often missing or outdated, particularly in the second-hand housing market. This study proposes a multi-task learning model that predicts these attributes from facade images. The model jointly estimates the construction year, building structure, and property type, from which the structural fireproof class - defined as H (non-fireproof), T (semi-fireproof), or M (fireproof) - is derived via a rule-based mapping based on official insurance criteria. We trained and evaluated the model using a large-scale dataset of Japanese residential images, applying rigorous filtering and deduplication. The model achieved high accuracy in construction-year regression and robust classification across imbalanced categories. Qualitative analyses show that it captures visual cues related to building age and materials. Our approach demonstrates the feasibility of scalable, interpretable, image-based risk-profiling systems, offering potential applications in insurance, urban planning, and disaster preparedness.