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September 10, 2025Remote SensingOpen Access

Stepwise Building Damage Estimation Through Time-Scaled Multi-Sensor Integration: A Case Study of the 2024 Noto Peninsula Earthquake

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Significant improvement in damage estimation accuracy was observed with integrated data sources.
The framework achieved a mean absolute percentage error of 25.0% for composite damage estimation.
Incorporating social media data and IoT can further enhance assessment accuracy during disasters.
Timely updates of building damage estimates are crucial for effective emergency response and decision-making.

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Implication

Observational analysis improves building damage estimates from multi-sensor data in disaster response, suggesting enhanced accuracy for future events.

Cite This Study

Kimijima et al. (2025) studied this question.

synapsesocial.com/papers/68c1a12754b1d3bfb60dbf66 https://doi.org/10.3390/rs17152638

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Implication

Observational analysis improves building damage estimates from multi-sensor data in disaster response, suggesting enhanced accuracy for future events.

Stepwise Building Damage Estimation Through Time-Scaled Multi-Sensor Integration: A Case Study of the 2024 Noto Peninsula Earthquake

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Discussion

Implication

Cite This Study

Stepwise Building Damage Estimation Through Time-Scaled Multi-Sensor Integration: A Case Study of the 2024 Noto Peninsula Earthquake

Key Points

Discussion

Implication

Cite This Study

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