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
Viewed
Total article views: 5,550 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 May 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 4,104 | 1,359 | 87 | 5,550 | 79 | 63 |
- HTML: 4,104
- PDF: 1,359
- XML: 87
- Total: 5,550
- BibTeX: 79
- EndNote: 63
Total article views: 4,254 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 Feb 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,546 | 639 | 69 | 4,254 | 74 | 53 |
- HTML: 3,546
- PDF: 639
- XML: 69
- Total: 4,254
- BibTeX: 74
- EndNote: 53
Total article views: 1,296 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 May 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 558 | 720 | 18 | 1,296 | 5 | 10 |
- HTML: 558
- PDF: 720
- XML: 18
- Total: 1,296
- BibTeX: 5
- EndNote: 10
Viewed (geographical distribution)
Total article views: 5,550 (including HTML, PDF, and XML)
Thereof 5,321 with geography defined
and 229 with unknown origin.
Total article views: 4,254 (including HTML, PDF, and XML)
Thereof 4,120 with geography defined
and 134 with unknown origin.
Total article views: 1,296 (including HTML, PDF, and XML)
Thereof 1,201 with geography defined
and 95 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
22 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
- A Synthetic Aperture Radar Imaging Simulation Method for Sea Surface Scenes Combined with Electromagnetic Scattering Characteristics Y. He et al. 10.3390/rs16173335
- 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
- Deep Ensemble Learning for Rapid Large-Scale Postearthquake Damage Assessment: Application to Satellite Images from the 2023 Türkiye Earthquakes M. Soleimani-Babakamali et al. 10.1061/AOMJAH.AOENG-0043
- Обґрунтування переваг у використанні оптичних та радарних даних дзз при виявленні будівель, порушених внаслідок природного чи антропогенного впливу Л. Скрипник et al. 10.36023/ujrs.2024.11.4.277
- Scalable variational learning for noisy-OR Bayesian networks with normalizing flows for complex cascading disaster systems X. Li & S. Xu 10.1038/s44304-025-00080-9
- 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
- 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
- Flood change detection model based on an improved U-net network and multi-head attention mechanism F. Wang & X. Feng 10.1038/s41598-025-87851-6
- Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery C. Moreno González et al. 10.3390/math13071041
- Estimating Road Disruptions in Urban Contexts Due to Earthquakes Using Machine Learning Surrogates C. Costa & V. Silva 10.1002/eqe.4318
- Natural disaster damage analysis using lightweight spatial feature aggregated deep learning model K. Abraham et al. 10.1007/s12145-024-01325-3
- Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings N. Karimi et al. 10.1016/j.culher.2024.05.009
- Unsupervised SAR Image Change Detection Based on Curvelet Fusion and Local Patch Similarity Information Clustering Y. Huang et al. 10.3390/rs17050840
- Assessing the Impact of the 2023 Kahramanmaras Earthquake on Cultural Heritage Sites Using High-Resolution SAR Images C. Boyoğlu et al. 10.3390/heritage6100349
- 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
- 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
- Long-term tracking of recovery of built infrastructure after wildfires with deep network topologies A. Schmidt et al. 10.1007/s00521-025-11003-0
22 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
- A Synthetic Aperture Radar Imaging Simulation Method for Sea Surface Scenes Combined with Electromagnetic Scattering Characteristics Y. He et al. 10.3390/rs16173335
- 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
- Deep Ensemble Learning for Rapid Large-Scale Postearthquake Damage Assessment: Application to Satellite Images from the 2023 Türkiye Earthquakes M. Soleimani-Babakamali et al. 10.1061/AOMJAH.AOENG-0043
- Обґрунтування переваг у використанні оптичних та радарних даних дзз при виявленні будівель, порушених внаслідок природного чи антропогенного впливу Л. Скрипник et al. 10.36023/ujrs.2024.11.4.277
- Scalable variational learning for noisy-OR Bayesian networks with normalizing flows for complex cascading disaster systems X. Li & S. Xu 10.1038/s44304-025-00080-9
- 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
- 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
- Flood change detection model based on an improved U-net network and multi-head attention mechanism F. Wang & X. Feng 10.1038/s41598-025-87851-6
- Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery C. Moreno González et al. 10.3390/math13071041
- Estimating Road Disruptions in Urban Contexts Due to Earthquakes Using Machine Learning Surrogates C. Costa & V. Silva 10.1002/eqe.4318
- Natural disaster damage analysis using lightweight spatial feature aggregated deep learning model K. Abraham et al. 10.1007/s12145-024-01325-3
- Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings N. Karimi et al. 10.1016/j.culher.2024.05.009
- Unsupervised SAR Image Change Detection Based on Curvelet Fusion and Local Patch Similarity Information Clustering Y. Huang et al. 10.3390/rs17050840
- Assessing the Impact of the 2023 Kahramanmaras Earthquake on Cultural Heritage Sites Using High-Resolution SAR Images C. Boyoğlu et al. 10.3390/heritage6100349
- 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
- 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
- Long-term tracking of recovery of built infrastructure after wildfires with deep network topologies A. Schmidt et al. 10.1007/s00521-025-11003-0
Latest update: 18 Apr 2025
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...
Altmetrics
Final-revised paper
Preprint