Articles | Volume 23, issue 2
https://doi.org/10.5194/nhess-23-789-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-789-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Feb 2023
Research article |
| 23 Feb 2023
| 23 Feb 2023
Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning
Anirudh Rao
CORRESPONDING AUTHOR
Seismic Risk Team, Global Earthquake Model Foundation, Pavia, Italy
Jungkyo Jung
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Vitor Silva
Seismic Risk Team, Global Earthquake Model Foundation, Pavia, Italy
Giuseppe Molinario
World Bank Group, Washington, DC, USA
Sang-Ho Yun
Earth Observatory of Singapore, Nanyang Technological University, Singapore
Asian School of the Environment, Nanyang Technological University, Singapore
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
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Cited
14 citations as recorded by crossref.
- SDCDNet: A Semi-Dual Change Detection Network Framework With Super-Weak Label for Remote Sensing Image J. Wang et al. 10.1109/TGRS.2023.3286113
- Unsupervised learning framework for region-based damage assessment on xBD, a large satellite imagery P. Mittal et al. 10.1007/s11069-023-06074-y
- Natural disaster damage analysis using lightweight spatial feature aggregated deep learning model K. Abraham et al. 10.1007/s12145-024-01325-3
- A Synthetic Aperture Radar Imaging Simulation Method for Sea Surface Scenes Combined with Electromagnetic Scattering Characteristics Y. He et al. 10.3390/rs16173335
- Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings N. Karimi et al. 10.1016/j.culher.2024.05.009
- A Novel UNet 3+ Change Detection Method Considering Scale Uncertainty in High-Resolution Imagery T. Bai et al. 10.3390/rs16111846
- Advancing Rural Building Extraction via Diverse Dataset Construction and Model Innovation with Attention and Context Learning M. Yu et al. 10.3390/app132413149
- Assessing the Impact of the 2023 Kahramanmaras Earthquake on Cultural Heritage Sites Using High-Resolution SAR Images C. Boyoğlu et al. 10.3390/heritage6100349
- Evaluating fine tuned deep learning models for real-time earthquake damage assessment with drone-based images F. Kizilay et al. 10.1007/s43503-024-00034-6
- InCR: Inception and concatenation residual block-based deep learning network for damaged building detection using remote sensing images B. Tasci et al. 10.1016/j.jag.2023.103483
- DDFormer: A Dual-Domain Transformer for Building Damage Detection Using High-Resolution SAR Imagery T. Li et al. 10.1109/LGRS.2023.3288007
- Data collection tools for post-disaster damage assessment of building and lifeline infrastructure systems J. Lozano & I. Tien 10.1016/j.ijdrr.2023.103819
- Vibration-based building health monitoring using spatio-temporal learning model V. Dang & H. Pham 10.1016/j.engappai.2023.106858
- Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey J. Gu et al. 10.3390/buildings14040898
14 citations as recorded by crossref.
- SDCDNet: A Semi-Dual Change Detection Network Framework With Super-Weak Label for Remote Sensing Image J. Wang et al. 10.1109/TGRS.2023.3286113
- Unsupervised learning framework for region-based damage assessment on xBD, a large satellite imagery P. Mittal et al. 10.1007/s11069-023-06074-y
- Natural disaster damage analysis using lightweight spatial feature aggregated deep learning model K. Abraham et al. 10.1007/s12145-024-01325-3
- A Synthetic Aperture Radar Imaging Simulation Method for Sea Surface Scenes Combined with Electromagnetic Scattering Characteristics Y. He et al. 10.3390/rs16173335
- Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings N. Karimi et al. 10.1016/j.culher.2024.05.009
- A Novel UNet 3+ Change Detection Method Considering Scale Uncertainty in High-Resolution Imagery T. Bai et al. 10.3390/rs16111846
- Advancing Rural Building Extraction via Diverse Dataset Construction and Model Innovation with Attention and Context Learning M. Yu et al. 10.3390/app132413149
- Assessing the Impact of the 2023 Kahramanmaras Earthquake on Cultural Heritage Sites Using High-Resolution SAR Images C. Boyoğlu et al. 10.3390/heritage6100349
- Evaluating fine tuned deep learning models for real-time earthquake damage assessment with drone-based images F. Kizilay et al. 10.1007/s43503-024-00034-6
- InCR: Inception and concatenation residual block-based deep learning network for damaged building detection using remote sensing images B. Tasci et al. 10.1016/j.jag.2023.103483
- DDFormer: A Dual-Domain Transformer for Building Damage Detection Using High-Resolution SAR Imagery T. Li et al. 10.1109/LGRS.2023.3288007
- Data collection tools for post-disaster damage assessment of building and lifeline infrastructure systems J. Lozano & I. Tien 10.1016/j.ijdrr.2023.103819
- Vibration-based building health monitoring using spatio-temporal learning model V. Dang & H. Pham 10.1016/j.engappai.2023.106858
- Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey J. Gu et al. 10.3390/buildings14040898
Latest update: 13 Dec 2024
Short summary
This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets including high-resolution building inventories, while also leveraging recent advances in machine-learning algorithms. For three out of the four recent earthquakes studied, the machine-learning framework is able to identify over 50 % or nearly half of the damaged buildings successfully.
This article presents a framework for semi-automated building damage assessment due to...
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