Articles | Volume 17, issue 10
https://doi.org/10.5194/essd-17-5259-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-5259-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The 2024 Noto Peninsula earthquake building damage dataset: multi-source visual assessment
Ruben Vescovo
Department of Civil and Environmental Engineering, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai, 980-8572, Japan
Bruno Adriano
International Research Institute of Disaster Science (IRIDeS), Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai, 980-8572, Japan
Sesa Wiguna
Department of Civil and Environmental Engineering, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai, 980-8572, Japan
Chia Yee Ho
Department of Civil and Environmental Engineering, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai, 980-8572, Japan
Jorge Morales
Department of Civil and Environmental Engineering, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai, 980-8572, Japan
Xuanyan Dong
Department of Civil and Environmental Engineering, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai, 980-8572, Japan
Shin Ishii
Department of Civil and Environmental Engineering, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai, 980-8572, Japan
Kazuki Wako
Department of Civil and Environmental Engineering, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai, 980-8572, Japan
Yudai Ezaki
Department of Civil Engineering and Architecture, School of Engineering, Aoba-6-6-06 Aramaki, Aoba Ward, Sendai, Miyagi, 980-8572, Japan
Ayumu Mizutani
International Research Institute of Disaster Science (IRIDeS), Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai, 980-8572, Japan
Erick Mas
International Research Institute of Disaster Science (IRIDeS), Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai, 980-8572, Japan
Satoshi Tanaka
Faculty of Social and Environmental Studies, Department of Social and Environmental Studies, Tokoha University, Yayoi-cho 6-1, Suruga-ku, Shizuoka city, 422-8581, Japan
Shunichi Koshimura
CORRESPONDING AUTHOR
International Research Institute of Disaster Science (IRIDeS), Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai, 980-8572, Japan
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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.
We compiled an inventory of building condition (destroyed vs. survived) following the 2024 Noto...
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