Articles | Volume 17, issue 10
https://doi.org/10.5194/essd-17-5259-2025
https://doi.org/10.5194/essd-17-5259-2025
Data description paper
 | 
10 Oct 2025
Data description paper |  | 10 Oct 2025

The 2024 Noto Peninsula earthquake building damage dataset: multi-source visual assessment

Ruben Vescovo, Bruno Adriano, Sesa Wiguna, Chia Yee Ho, Jorge Morales, Xuanyan Dong, Shin Ishii, Kazuki Wako, Yudai Ezaki, Ayumu Mizutani, Erick Mas, Satoshi Tanaka, and Shunichi Koshimura

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Cited articles

Anniballe, R., Noto, F., Scalia, T., Bignami, C., Stramondo, S., Chini, M., and Pierdicca, N.: Earthquake damage mapping: An overall assessment of ground surveys and VHR image change detection after L'Aquila 2009 earthquake, Remote Sens. Environ., 210, 166–178, https://doi.org/10.1016/j.rse.2018.03.004, 2018. a
Barrington, L., Ghosh, S., Greene, M., Har-Noy, S., Berger, J., Gill, S., Yu-Min, A., and Huyck, C.: Crowdsourcing earthquake damage assessment using remote sensing imagery, Ann. Geophys., 54, https://doi.org/10.4401/ag-5324, 2012. a
British Broadcasting Corporation: Japan earthquake: Fires hit quake zone as rescuers race to reach survivors, BBC News, https://www.bbc.com/news/world-asia-67865502, last access: 25 April 2024. a
CEMS: Detection methods and Damage Assessment, Online, CEMS, https://mapping.emergency.copernicus.eu/about/rapid-mapping-manual/detection-methods-damage-assessment/, last access: 10 June 2025. a, b, c
Charvet, I., Suppasri, A., and Imamura, F.: Empirical fragility analysis of building damage caused by the 2011 Great East Japan tsunami in Ishinomaki city using ordinal regression, and influence of key geographical features, Stochastic Environmental Research and Risk Assessment, https://doi.org/10.1007/s00477-014-0850-2, 2014. a
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Short summary
We compiled an inventory of building condition (destroyed vs. survived) following the 2024 Noto Peninsula earthquake for the entire affected area, totaling over 140 000 structures. We discuss how we fused freely available data of different types and from different sources to generate the final dataset. We show that our method produces highly accurate results relative to those obtained by on-site surveys. These data can be used to train AI to quickly detect damaged structures in future disasters.
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