---
title: Estimation of fireproof structure class and construction year for disaster risk assessment
tags:  #国際会議  
author: [Okamoto Lab. (The Univ. of Electro-Communications)](https://convert.docswell.com/user/okmt_lab)
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description: Hibiki Ayabe, Kazushi Okamoto, Atsushi Shibata, Kei Harada, Koki Karube: Estimation of fireproof structure class and construction year for disaster risk assessment, ACM Multimedia Asia 2025 (MMAsia 2025) Workshop Visual and Signal Communication Technologies in Design of Housing, Urban Spaces, Local Communities, and Human Behavior, 2025.12, Kuala Lumpur, Malaysia.
published: December 09, 25
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---
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Estimation of Fireproof Structure Class and
Construction Year for Disaster Risk Assessment
，Kazushi Okamoto，Atsushi Shibata，Kei Harada，Koki Karube
Hibiki Ayabe
The University of Electro-Communications
Dec 9, 2025
ACM MM Asia 2025 Workshop
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Background and Motivation
Administrative Use Case
Background: Municipal administrations need
construction year and fireproof structure class for
disaster risk assessment. The fireproof class is a legal
rating derived from building structure and property type
that indicates how long a building can withstand fire.
Challenge: However, building metadata containing this
information is often missing or difficult to obtain.
Potential Approach: Fireproof class can be inferred from
facade images, easing data collection and supporting
city-scale risk mapping.
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Related Work
[Hafidz+ ,2024]: Building typology classification using CNNs for city-scale seismic
vulnerability assessment in Indonesia.
[Chen+, 2022]: Multi-task CNN–based estimation of structural attributes from Street View
images for flood risk assessment in the U.S.
[Ogawa+, 2023]: Prediction of Japanese building structure and construction year using CNNs
and Vision Transformers.
Gap: While these studies successfully infer building structures and related attributes, none
of the approaches address Japan-specific fireproof class that are essential for domestic firerisk evaluation.
We derive Japan&#039;s fireproof class from predicted building structure and
property type, ensuring interpretability while using only facade imagery.
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Defining Our Research Questions (RQs)
RQ1: Can disaster-relevant building attributes be accurately estimated from residential
facade images using a multi-task learning (MTL)?
RQ2: How effectively can the fireproof class be inferred from intermediate attribute
predictions in a hierarchical modeling approach?
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Task Definition (Task1, 2, 3 &amp; 4)
Task4: Fireproof Structure Class
Building Structure
Property Type
Fireproof Class
Concrete
Any
M (Fireproof)
Steel
Communal
M (Fireproof)
Steel
Wooden
Dec 9, 2025
Non-communal T (Semi-fireproof)
Any
H (Non-fireproof)
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Multi-Task Model Design
Uncertainty-weighted loss [Kendall+,2018]: dynamically weight and combine regression
and classification losses.
The construction year regression (Task1) acts as an anchor role, helping to stabilize multitask training and balance gradients across tasks.
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Dataset and Preprocessing
Dataset: Large-scale LIFULL HOME&#039;S data (approx. 2.77M training images).[LIFULL,2015]
Filtering: Rigorous steps including deduplication and filtering with CLIP to ensure highquality &quot;Entire residential property&quot; images.
Representative Sample Facade
Year: 2004
Communal / Concrete
Year: 1997
Year: 1966
→ Fireproof class: M (Fireproof)
Non-communal / Steel
Non-communal / Wooden
→ Fireproof class: T (Semi-fireproof) → Fireproof class: H (Non-fireproof)
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Results I: Construction Year Prediction (Task1)
Learning Rate (LR) MAE ↓ RMSE ↓ MedAE ↓
4.97
6.88
3.59
6.02
7.78
4.90
Considering the MAE (10.689) and RMSE (12.121) reported for CNN-based models by Li et al.
[Li+, 2018], our approach achieves favorable estimation accuracy.
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Results II: Intermediate Classification (Tasks2&amp;3)
LR
Structure Acc ↑ Structure Macro F1 ↑ Type Accuracy ↑ Type Macro F1 ↑
1e-5
0.9275
0.8714
0.8321
0.7945
1e-6
0.9182
0.8582
0.8147
0.7721
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Summary of RQ1: Feasibility and Interpretation
RQ1: Can disaster-relevant building attributes be accurately estimated from residential facade
images using an MTL?
Answer: The MTL model can estimate disaster-relevant attributes from facade images with
practical accuracy
construction year MAE 4.97 years
building structure acc 92.75%
property type acc 83.21%
indicating reliable extraction of core features for risk assessment.
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Results III: Fireproof Class Derivation (Task4)
LR
Acc ↑
Macro F1 ↑ Weighted F1 ↑
F1 (H / M / T) ↑
1e-5 0.8916
0.6459
0.8900
0.7880 / 0.9304 / 0.2194
1e-6 0.8802
0.6227
0.8789
0.7692 / 0.9223 / 0.1767
Performance for the T (Semi-fireproof) class is low, primarily due to dataset imbalance.
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Qualitative Analysis: Interpretation and Error Propagation
Negative samples
Pred: Steel / Communal →M
Pred: Steel / Non-communal →T Pred: Steel / Non-communal →T
True: Steel / Communal →M
True: Wooden / Communal →H
True: Wooden / Non-communal →H
By splitting the tasks, we can understand where errors occur in the intermediate attributes,
ensuring that explainability is maintained.
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Summary of RQ2: Robustness of Hierarchy
RQ2: How effectively can the fireproof class be inferred from intermediate attribute
predictions in a hierarchical modeling approach?
Answer: Hierarchical rule-based mapping achieves high overall accuracy (89.16%), but the Tclass F1 is low (~0.22) and requires improvement, recommend data reweighting and Tfocused model refinements.
Finding: Even when intermediate attribute predictions are incorrect, the final fireproof class
remains correct in about 12% of cases, indicating partial error resilience.
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Conclusion
Summary
This study presents an MTL model for estimating disaster-relevant building attributes
from facade images, achieving practical accuracy.
The model addresses challenges in building metadata by inferring attributes directly from
exterior imagery, enhancing disaster risk assessment.
Contribution
Introduces a novel approach for estimating fireproof structure classes and construction
years, tailored to Japan&#039;s specific standards.
Demonstrates the effectiveness of hierarchical rule mapping in inferring fireproof classes,
highlighting areas for improvement in dataset balance and model refinement.
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Future Work
1. Improving Class Balance: Address dataset imbalance for the T-class through reweighting
and data augmentation techniques to enhance prediction accuracy across all fireproof
categories.
2. Interpretability Enhancement: Apply explainability methods such as Grad-CAM and LIME
to visualize which facade regions contribute to attribute predictions, strengthening
model transparency for stakeholders.
3. Regional Risk Assessment: Integrate predictions with Google Street View imagery at
scale to generate city-wide and region-specific disaster risk maps for urban planning and
emergency preparedness.
4. Global Disaster Risk Framework: Extend the approach beyond Japan by adapting the
methodology to diverse building standards and construction practices across different
countries and regions.
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References
[Hafidz+, 2024] Hafidz R. Firmansyah et al. 2024. Building typology classification using CNNs
for city-scale rapid seismic vulnerability assessment. Eng. Appl. Artif. Intell. 131.
[Chen+, 2022] F.-C. Chen et al. 2022. Deep learning-based building attribute estimation from
Google Street View images for flood risk assessment. J. Comput. Civ. Eng. 36, 6.
[Ogawa+, 2023]Y. Ogawa et al. 2023. Deep Learning Approach for Classifying the Built Year
and Structure of Individual Buildings by Automatically Linking Street View Images and GIS
Building Data. IEEE J. of Selected Topics in Applied Earth Observ. and Remote Sens. 16.
[Kendall+, 2018] A. Kendall et al. 2018. Multi-task learning using uncertainty to weigh losses
for scene geometry and semantics.
[LIFULL, 2015] LIFULL Co. 2015. LIFULL HOME’S Dataset. doi:10.32130/idr.6.0
[Li+, 2018] Y. Li et al. 2018. Estimating building age from Google Street View images using
deep learning.
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