Articles | Volume 23, issue 2
https://doi.org/10.5194/nhess-23-789-2023
https://doi.org/10.5194/nhess-23-789-2023
Research article
 | 
23 Feb 2023
Research article |  | 23 Feb 2023

Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning

Anirudh Rao, Jungkyo Jung, Vitor Silva, Giuseppe Molinario, and Sang-Ho Yun

Viewed

Total article views: 6,092 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,537 1,466 89 6,092 85 78
  • HTML: 4,537
  • PDF: 1,466
  • XML: 89
  • Total: 6,092
  • BibTeX: 85
  • EndNote: 78
Views and downloads (calculated since 18 May 2022)
Cumulative views and downloads (calculated since 18 May 2022)

Viewed (geographical distribution)

Total article views: 6,092 (including HTML, PDF, and XML) Thereof 5,856 with geography defined and 236 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 12 Jul 2025
Download
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.
Share
Altmetrics
Final-revised paper
Preprint