Preprints
https://doi.org/10.5194/nhess-2022-125
https://doi.org/10.5194/nhess-2022-125
 
18 May 2022
18 May 2022
Status: a revised version of this preprint is currently under review for the journal NHESS.

Earthquake Building Damage Detection based on Synthetic Aperture Radar Imagery and Machine Learning

Anirudh Rao1, Jungkyo Jung2, Vitor Silva1, Giuseppe Molinario3, and Sang-Ho Yun4,5,6 Anirudh Rao et al.
  • 1Global Earthquake Model Foundation, Pavia, Italy
  • 2Jet Propulsion Laboratory, California Institute of Technology, CA, USA
  • 3World Bank Group, Washington DC, USA
  • 4Earth Observatory of Singapore, Nanyang Technological University, Singapore
  • 5Asian School of the Environment, Nanyang Technological University, Singapore
  • 6School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

Abstract. This article presents a framework for semi-automated building damage assessment due to earthquakes from remote sensing data and other supplementary datasets, while also leveraging recent advances in machine-learning algorithms. The framework integrates high-resolution building inventory data with earthquake ground shaking intensity maps and surface-level changes detected by comparing pre- and post-event InSAR (interferometric synthetic aperture radar) images. We demonstrate the use of ensemble models in a machine-learning approach to classify the damage state of buildings in the area affected by an earthquake. Both multi-class and binary damage classification are attempted for four recent earthquakes and we compare the predicted damage labels with ground truth damage grade labels reported in field surveys. For three out of the four earthquakes studied, the model is able to identify over fifty percent or nearly half of the damaged buildings successfully when using binary classification. Multi-class damage grade classification using InSAR data has rarely been attempted previously, and the case studies presented in this report represent one of the first such attempts using InSAR data.

Anirudh Rao et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-125', Samuel Roeslin, 29 May 2022
    • AC1: 'Reply on RC1', Anirudh Rao, 11 Aug 2022
  • RC2: 'Comment on nhess-2022-125', Rui Jesus, 20 Jun 2022
    • AC2: 'Reply on RC2', Anirudh Rao, 11 Aug 2022

Anirudh Rao et al.

Anirudh Rao et al.

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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 fifty percent or nearly half of the damaged buildings successfully.
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