A Deep Learning Application for Building Damage Assessment Using Ultra-High-Resolution Remote Sensing Imagery in Turkey Earthquake

作     者：Haobin Xia Jianjun Wu Jiaqi Yao Hong Zhu Adu Gong Jianhua Yang Liuru Hu Fan Mo Haobin Xia;Jianjun Wu;Jiaqi Yao;Hong Zhu;Adu Gong;Jianhua Yang;Liuru Hu;Fan Mo

作者机构：Academy of Ecological Civilization Development for JingJin-JiTianjin Normal UniversityTianjin 300387China Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China College of Ecology and EnvironmentInstitute of Disaster PreventionLangfang 065201China Dpto.de Ingeniería CivilEscuela Politécnica Superior de AlicanteUniversidad de AlicanteE-03080 AlicanteSpain Land Satellite Remote Sensing Application CenterMinistry of Natural ResourcesBeijing 100048China 

出 版 物：《International Journal of Disaster Risk Science》 (国际灾害风险科学学报（英文版）)

年 卷 期：2023年第14卷第6期

页      面：947-962页

核心收录：

学科分类：12[管理学] 081405[工学-防灾减灾工程及防护工程] 081802[工学-地球探测与信息技术] 07[理学] 08[工学] 083002[工学-环境工程] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 070801[理学-固体地球物理学] 081104[工学-模式识别与智能系统] 0708[理学-地球物理学] 0818[工学-地质资源与地质工程] 0815[工学-水利工程] 0706[理学-大气科学] 081602[工学-摄影测量与遥感] 0816[工学-测绘科学与技术] 0835[工学-软件工程] 0814[工学-土木工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Third Xinjiang Scientific Expedition Program(Grant 2022xjkk0600) 

主　　题：BDANet Building damage assessment Deep learning Disaster assessment Emergency rescue Ultra-high-resolution remote sensing 

摘      要：Rapid building damage assessment following an earthquake is important for humanitarian relief and disaster emergency *** February 2023,two magnitude-7.8 earthquakes struck Turkey in quick succession,impacting over 30 major cities across nearly 300 km.A quick and comprehensive understanding of the distribution of building damage is essential for e fficiently deploying rescue forces during critical rescue *** article presents the training of a two-stage convolutional neural network called BDANet that integrated image features captured before and after the disaster to evaluate the extent of building damage in *** on high-resolution remote sensing data from WorldView2,BDANet used predisaster imagery to extract building outlines;the image features before and after the disaster were then combined to conduct building damage *** optimized these results to improve the accuracy of building edges and analyzed the damage to each building,and used population distribution information to estimate the population count and urgency of rescue at different disaster *** results indicate that the building area in the Islahiye region was 156.92 ha,with an affected area of 26.60 *** damaged buildings accounted for 15.67%of the total building area in the affected *** population distribution data indicated approximately 253,297,and 1,246 people in the collapsed,severely damaged,and lightly damaged areas,*** verification showed that the BDANet model exhibited good performance in handling high-resolution images and can be used to directly assess building damage and provide rapid information for rescue operations in future disasters using model weights.