基于Deeplab V3+的2019年长宁M6.0地震建筑物震害信息提取研究

doi:10.12196/j.issn.1000-3274.2022.03.009

Abstract

Abstract: Rapid acquisition of building damage information caused by earthquakes is the key to post-disaster rescue and disaster loss assessment. The development of deep learning technology represented by semantic segmentation network provides a new means for the acquisition of building damage information after earthquakes. In this paper, Deeplab V3 + neural network model is applied to the extraction of earthquake damage information of buildings. The UAV image with 0.06 meters resolution in Shuanghe Town after Chang Ning earthquake is used as the data source for the extraction of earthquake damage information of buildings. The buildings in the test area are divided into two categories: no obvious damage and damage, and the rest are used as background information. The seismic damage information of buildings is classified and annotated manually, and the sample data set is constructed and the network model is trained. In order to improve the robustness and generalization ability of the model for the classification of ground objects in different scenarios, the sample enhancement process was carried out, and the experiment of the effect of sample enhancement on the model accuracy and the effect of the number of training samples on the model accuracy were carried out. After the earthquake, the 0.4-meter-resolution UAV image was used to conduct the model transferability experiment. The experimental results show that the neural network model established in this study can achieve higher extraction accuracy in the extraction of building earthquake damage information; the extraction accuracy of each category of the model after sample enhancement is improved to varying degrees, especially it is that the recall rate of the destroyed building is increased by about 0.15, and the precision rate is increased by about 0.20; the neural network model has a certain transferability without any modification.

Key words: Deep learning, Deeplab V3+, Building, Earthquake damage extraction, Sample enhancement

CLC Number:

TP79

WANG Xin, DOU Ai-xia, GUO Hong-mei, YUAN Xiao-xiang. Research on Extraction of Building Damage Information in the 2019 Changning M6.0 Earthquake Based on Deeplab V3+[J]. EARTHQUAKE, 2022, 42(3): 124-140.

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Research on Extraction of Building Damage Information in the 2019 Changning M6.0 Earthquake Based on Deeplab V3+

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