BDANet-based assessment of building damage from earthquake disasters

doi:10.6046/zrzyyg.2023164

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Abstract

The rapid assessment of building damage following destructive earthquakes serves as a critical foundation for decision-making and technical guarantee in post-earthquake scientific evaluations, holding great significance in humanitarian aid and emergency response. This study aims to overcome the challenge in rapidly quantifying the number of buildings affected. Considering that most existing post-earthquake building damage assessments based on remote sensing images rely on pre- and post-disaster image segmentation, this study, by using the U-Net deep convolutional neural network as the main model, introduced a three-stage convolutional neural network for building damage assessment (BDANet) framework that integrates assessment and prediction for post-earthquake building damage information. First, the encoder-decoder network structure of U-Net was used to extract building location information. Second, building damage was assessed using pre- and post-disaster images to localize and grade damage in the image segmentation results. Finally, the number of buildings damaged at various levels was predicted to support post-disaster rescue and reconstruction efforts. The study evaluated and quantified the levels of post-earthquake building damage in the M7.1 earthquake in Morelos State, central Mexico in 2017 and the M7.8 earthquake in Türkiye in 2023, confirming the accuracy and reliability of the proposed method. The experimental findings provide timely and precise data and technical support for post-disaster risk assessment.

Keywords remote sensing image earthquake disaster building damage damage assessment U-Net

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TP79

Issue Date: 23 December 2024

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	Jinling ZHAO
	Jian HUANG
	Zijun LIANG
	Xuedan ZHAO
	Tao JIN
	Hanghang GE
	Xiaoyan WEI
	Yuanzheng SHAO

Cite this article:

Jinling ZHAO,Jian HUANG,Zijun LIANG, et al. BDANet-based assessment of building damage from earthquake disasters[J]. Remote Sensing for Natural Resources, 2024, 36(4): 193-200.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023164 OR https://www.gtzyyg.com/EN/Y2024/V36/I4/193

Tab.1 Seismic image data source information

Fig.1 Examples of partially annotated images

Fig.2 Network structure for evaluating and predicting building damage levels

Fig.3 Visualization of earthquake disaster images and annotations

Tab.2 Model parameter

Tab.3 Assessment results of building damage

Tab.4 Statistics of experimental results

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