[1]LEI Yating,SHEN Zhanfeng,XU Zeyu,et al.Evaluation of the Damaged Buildings in the 2014 Ludian MS6.5 Earthquake in Yunnan and Their Post-earthquake Reconstruction Based on D-LinkNet[J].Journal of Seismological Research,2022,45(04):608-616.[doi:10.20015/j.cnki.ISSN1000-0666.2022.0063]
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Evaluation of the Damaged Buildings in the 2014 Ludian MS6.5 Earthquake in Yunnan and Their Post-earthquake Reconstruction Based on D-LinkNet

References:

程希萌,沈占锋,邢廷炎,等.2016.基于高分遥感影像的地震受灾建筑物提取与倒损情况快速评估[J].自然灾害学报,25(3):22-31.
杜浩国,张方浩,卢永坤,等.2021,基于多源遥感影像的2021年云南漾濞MS6.4地震灾区建筑物信息识别与震害分析[J].地震研究,44(3):490-498.
杜妍开,龚丽霞,李强.2020.基于最优分割的高分辨率遥感影像震害建筑物识别技术[J].地震学报,42(6):760-768.
和嘉吉,卢永坤,代博洋,等.2015.鲁甸MS6.5与景谷MS6.6地震灾区房屋抗震能力差异分析[J].地震研究,38(1):137-142.
庞卫东,杨润海,陈俊磊,等.2016.2014年鲁甸MS6.5地震龙头山镇场地高密度电法勘探[J].地震研究,39(4):622-629.
朴永军.2013.云南省青海省房屋地震易损性研究[D].哈尔滨:中国地震局工程力学研究所.
眭海刚,刘超贤,黄立洪,等.2019.遥感技术在震后建筑物损毁检测中的应用[J].武汉大学学报(信息科学版),44(7):1008-1019.
田枥文.2020.基于深度学习的遥感地图多目标分割检测研究[D].成都:成都理工大学.
王树华,于会臻,谭绍泉,等.2020.基于深度卷积神经网络的地震相识别技术研究[J].物探化探计算技术,42(4):475-480.
王泽泓,刘厚泉.2019.基于迁移学习与自适应特征融合的建筑物识别[J].计算机技术与发展,29(12):40-43.
张德成.1993.建筑物震害航空照片目视判读标志的初步研究[J].地震,(1):26-30.
张立恒,王浩,薛博维,等.2021.基于改进D-LinkNet模型的高分遥感影像道路提取研究[J].计算机工程,47(9):288-296.
赵妍,张景发,姚磊华.2016.基于面向对象的高分辨率遥感建筑物震害信息提取与评估[J].地震学报,38(6):942-951.
朱祺琪,李真,张亚男,等.2021.全局局部细节感知条件随机场的高分辨率遥感影像建筑物提取[J].遥感学报,25(7):1422-1433.
Bialas J,Oommen T,Rebbapragada U,et al.2016.Object-based classification of earthquake damage from high-resolution optical imagery using machine learning[J].Journal of Applied Remote Sensing,10(3):036025.
Cooner A J,Shao Y,Campbell J B.2016.Detection of urban damage using remote sensing and machine learning algorithms:revisiting the 2010 Haiti Earthquake[J].Remote Sensing,8(10):868.
Davari M R,Momeni M,Moallem P.2019.Transferable object-based framework based on deep convolutional neural networks for building extraction[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,12(8):2627-2635.
Li Y,Zhang H,Xue X,et al.2018.Deep learning for remote sensing image classification:A survey[J].Wires Data Mining and Knowledge Discovery,8(6):E1264.
Liu P,Wei Y,Wang Q,et al.2020.Research on post-earthquake landslide extraction algorithm based on improved U-Net model[J].Remote Sensing,12(5):894-894.
Masarczyk W,Gomb P,Grabowski B,et al.2020.Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling[J].Remote Sensing,12(16):2653.
Matin S S,Pradhan B.2021.Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review[J].Geocarto International:1-27.
Song D,Tan X,Wang B,et al.2020.Integration of super-pixel segmentation and deep-learning methods for evaluating earthquake-damaged buildings using single-phase remote sensing imagery[J].International Journal of Remote Sensing,41(3):1040-1066.
Sorrentino L,Cattari S,Da Porto F,et al.2018.Seismic behaviour of ordinary masonry buildings during the 2016 Central Italy Earthquakes[J].Bulletin of Earthquake Engineering,17(10):5583-5607.
Taskin K G,Musaoglu N,Ersoy O K.2011.Damage assessment of 2010 Haiti Earthquake with post-earthquake satellite image by support vector selection and adaptation[J].Photogrammetric Engineering & Remote Sensing,77(10):1025-1035.
Uros M,Novak M S,Atalic J,et al.2020.Post-earthquake damage assessment of buildings—procedure for conducting building inspections[J].Gradevinar,72(12):1089-1115.
Xia L,Zhang X,Zhang J,et al.2021.Building extraction from very-High-resolution remote sensing images using semi-supervised semantic edge detection[J].Remote Sensing,13(11):2187.
Xiong C,Li Q S,Lu X Z.2020.Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network[J].Automation in Construction,109:102994.
Xu Y,Wu L,Xie Z,et al.2018.Building extraction in very high resolution remote sensing imagery using deep learning and guided filters[J].Remote Sensing,10(1):144.
Yang W T,Zhang X F,Luo P.2021.Transferability of convolutional neural network models for identifying damaged buildings due to earthquake[J].Remote Sensing,13(3):504.
Yuan S G,Yang K,Li X,et al.2020.Automatic seamline determination for urban image mosaicking based on road probability map from he D-LinkNet neural network[J].Sensors,20(7):1832.
Zhai W,Zhang J F,Xiao X L,et al.2021.Damaged building extraction from post-earthquake polsar data based on the fourier transform[J].Remote Sensing Letters,12(6):594-603.
Zhou L,Zhang C,Wu M.2018.D-LinkNet:Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery Road extraction[C]//IEEE Proceedings of the 31st IEEE/Cvf Conference on Computer Vision and Pattern Recognition,Salt Lake City:192-196.
Zhu Q,Li Z,Zhang Y,et al.2020.Building extraction from high spatial resolution remote sensing images via multiscale-aware and segmentation-prior conditional random fields[J].Remote Sensing,12(23):3983.
GB/T 17742—2020,中国地震烈度表[S].

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