Estimation of Fireproof Structure Class and Construction Year for Disaster Risk Assessment

TL;DR

This paper introduces a multi-task learning model that predicts building attributes from facade images to determine fireproof classification, aiding disaster risk assessment and insurance in Japan.

Contribution

It presents a novel image-based approach to estimate construction year and structure type, enabling scalable fireproof classification for risk management.

Findings

High accuracy in construction-year prediction

Robust classification across categories

Captures visual cues related to building age and materials

Abstract

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 However, key building metadata such as construction year and structure type are often missing or outdated, particularly in the second-hand housing market.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.