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

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Cited articles

Advanced Rapid Imaging and Analysis (ARIA): ARIA Damage Proxy Map for the 2015 Gorkha earthquake, Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology [data set], https://aria-share.jpl.nasa.gov/20150425-Nepal_EQ/DPM/, last access: 15 February 2023a. a
Advanced Rapid Imaging and Analysis (ARIA): ARIA Damage Proxy Map for the 2017 Puebla earthquake, Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology [data set], https://aria-share.jpl.nasa.gov/20170919-M7.1_Raboso_Mexico_EQ/DPM/, last access: 15 February 2023b. a
Advanced Rapid Imaging and Analysis (ARIA): ARIA Damage Proxy Map for the 2020 Puerto Rico earthquake, Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology [data set], https://aria-share.jpl.nasa.gov/20200106-Puerto_Rico_EQ/DPM/, last access: 15 February 2023c. a
Advanced Rapid Imaging and Analysis (ARIA): ARIA Damage Proxy Map for the 2020 Zagreb earthquake, Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology [data set], https://aria-share.jpl.nasa.gov/20200322_Zagreb_EQ/DPM/, last access: 15 February 2023d. a
Bai, Y., Adriano, B., Mas, E., and Koshimura, S.: Machine learning based building damage mapping from the ALOS-2/PALSAR-2 SAR imagery: Case study of 2016 Kumamoto earthquake, Journal of Disaster Research, 12, 646–655, https://doi.org/10.20965/jdr.2017.p0646, 2017. a, b, c
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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.
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