Advanced Search
Shuai X H，Jing S J，Zheng X X，Liu Q. 2022. Extraction of seismic damage information of buildings from three-dimensional images of oblique photography based on multi-scale segmentation and deep learning. Acta Seismologica Sinica，44（5）：881−890. DOI: 10.11939/jass.20220105
Citation: Shuai X H，Jing S J，Zheng X X，Liu Q. 2022. Extraction of seismic damage information of buildings from three-dimensional images of oblique photography based on multi-scale segmentation and deep learning. Acta Seismologica Sinica，44（5）：881−890. DOI: 10.11939/jass.20220105
shu

Extraction of seismic damage information of buildings from three-dimensional images of oblique photography based on multi-scale segmentation and deep learning

  • 1.

    China Earthquake Networks Center，Beijing 100045，China

  • 2.

    Henan Earthquake Agency，Zhengzhou 450016，China

  • 3.

    China Aero Geophysical Survey & Remote Sensing Center for Natural Resources，Beijing 100083，China

More Information
  • Received Date: June 16, 2022
  • Revised Date: August 07, 2022
  • Available Online: September 08, 2022
  • Published Date: September 14, 2022
    Graphical Abstract
    • Abstract
  • The method of combining multi-scale segmentation and deep learning is used to extract the seismic damage information of buildings from three-dimensional images of oblique photography after an earthquake. In this way, the comprehensive damage information of the roof and wall of the building is obtained. Taking the 2017 MS7.0 Jiuzhaigou earthquake as an example, firstly, based on the three-dimensional image of the roof and wall of the building, the multi-scale segmentation of samples are divided into three categories: intact building surface, damaged building surface, other ground objects and background. Secondly, 211 sample set with 100×100 pixel are selected to train the neural convolutional network model. The trained model is used to extract the seismic damage information of buildings in Qianguqing scenic spot and Zhangzha primary school. Finally, the accuracy of the extracted results is compared with the visual interpretation results. The results show that the extraction accuracy of damaged building surface is 65.5% and 71.1% respectively, the overall classification accuracy is 82.1% and 84.1% respectively, and the Kappa coefficient is 68.7% and 64.9% respectively. The result indicates that this method has certain advantages in seismic damage extraction of buildings from three-dimensional images of oblique photography.
    • FullText(HTML)
    • References (28)
  • 陈梦,王晓青. 2019. 全卷积神经网络在建筑物震害遥感提取中的应用研究[J]. 震灾防御技术,14(4):810–820. doi: 10.11899/zzfy20190412
    Chen M,Wang X Q. 2019. The study on extraction of seismic damage of buildings from remote sensing image based on fully convolutional neural network[J]. Technology for Earthquake Disaster Prevention,14(4):810–820 (in Chinese).
    范荣双,陈洋,徐启恒,王竞雪. 2019. 基于深度学习的高分辨率遥感影像建筑物提取方法[J]. 测绘学报,48(1):34–41. doi: 10.11947/j.AGCS.2019.20170638
    Fan R S,Chen Y,Xu Q H,Wang J X. 2019. A high-resolution remote sensing image building extraction method based on deep learning[J]. Acta Geodaetica et Cartographica Sinica,48(1):34–41 (in Chinese).
    冯丽英. 2017. 基于深度学习技术的高分辨率遥感影像建设用地信息提取研究[D]. 杭州: 浙江大学: 22–39.
    Feng L Y. 2017. Research on Construction Land Information Extraction From High Resolution Images With Deep Learning Technology[D]. Hangzhou: Zhejiang University: 22–39 （in Chinese）.
    高扬. 2018. 基于卷积神经网络的高分辨率遥感影像建筑物提取[D]. 南京: 南京大学: 37–51.
    Gao Y. 2018. Building Extraction From High Resolution Remote Sensing Images Based on Convolutional Neural Network[D]. Nanjing: Nanjing University: 37–51 （in Chinese）.
    荆帅军,帅向华,甄盟. 2019. 基于无人机倾斜影像的三维建筑物震害精细信息提取[J]. 地震学报,41(3):366–376.
    Jing S J,Shuai X H,Zhen M. 2019. Fine information extraction of 3D building seismic damage based on unmanned aerial vehicle oblique images[J]. Acta Seismologica Sinica,41(3):366–376 (in Chinese).
    李强. 2018. 多模式遥感数据地震应急关键技术研究[D]. 哈尔滨: 中国地震局工程力学研究所: 33–49.
    Li Q. 2018. Study on Key Technology of Earthquake Emergency Using Multi-Mode Remote Sensing Data[D]. Harbin: Institute of Engineering Mechanics, China Earthquake Administration: 33–49 （in Chinese）.
    李胜军. 2013. 基于倾斜航空影像的建筑物结构性损毁评估方法研究[D]. 成都: 西南交通大学: 12–20.
    Li S J. 2013. The Research of Building Structural Damage Assessment Based on Airborne Oblique Images[D]. Chengdu: Southwest Jiaotong University: 12–20 （in Chinese）.
    史路路. 2018. 基于卷积神经网络的遥感影像土地覆盖分类研究[D]. 北京: 中国科学院遥感与数字地球研究所: 14–18.
    Shi L L. 2018. Remote Sensing Images Land Cover Classification Based on Convolutional Neural Network[D]. Beijing: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences: 14–18 （in Chinese）.
    吴剑,陈鹏,刘耀林,王静. 2013. 震害损毁建筑物高分辨率遥感信息提取方法[J]. 地理与地理信息科学,29(3):35–38.
    Wu J,Chen P,Liu Y L,Wang J. 2013. Method of earthquake collapsed building information extraction based on high-resolution remote sensing[J]. Geography and Geo-Information Science,29(3):35–38 (in Chinese).
    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. doi: 10.11939/jass.2016.06.014
    Zhao Y,Zhang J F,Yao L H. 2016. Seismic damage information extraction and evaluation of buildings with high resolution remote sensing based on object-oriented method[J]. Acta Seismologica Sinica,38(6):942–951 (in Chinese).
    周阳,张云生,陈斯飏,邹峥嵘,朱耀晨,赵芮雪. 2019. 基于DCNN特征的建筑物震害损毁区域检测[J]. 国土资源遥感,31(2):44–50.
    Zhou Y,Zhang Y S,Chen S Y,Zou Z R,Zhu Y C,Zhao R X. 2019. Disaster damage detection in building areas based on DCNN features[J]. Remote Sensing for Land and Resources,31(2):44–50 (in Chinese).
    Gerke M,Kerle N. 2011. Automatic structural seismic damage assessment with airborne oblique pictometry© imagery[J]. Photogramm Eng Remote Sens,77(9):885–898. doi: 10.14358/PERS.77.9.885
    Hu Z W,Li Q Q,Zou Q,Zhang Q,Wu G F. 2016. A bilevel scale-sets model for hierarchical representation of large remote sensing images[J]. IEEE Trans Geosci Remote Sens,54(12):7366–7377. doi: 10.1109/TGRS.2016.2600636
    Lecun Y,Bottou L,Bengio Y,Haffner P. 1998. Gradient-based learning applied to document recognition[J]. Proc IEEE,86(11):2278–2324. doi: 10.1109/5.726791
    Sun G Y,Huang H,Zhang A Z,Li F,Zhao H M,Fu H. 2019. Fusion of multiscale convolutional neural networks for building extraction in very high-resolution images[J]. Remote Sens,11(3):227. doi: 10.3390/rs11030227
    Vetrivel A,Gerke M,Kerle N,Nex F,Vosselman G. 2018. Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images,and multiple-kernel-learning[J]. ISPRS J Photogramm Remote Sens,140:45–59.
    Yuan J Y. 2016. Automatic building extraction in aerial scenes using convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,40(11):2793–2798.
    • Related Articles

  • Related Articles

    Chen Kai, Pan Hua. 2025: Machine learning-based regional ground seismic motion simulation：A case study of the MS6.8 Luding earthquake in 2022. Acta Seismologica Sinica, 47(2): 242-253. DOI: 10.11939/jass.20230084
    Liu Wei, Huang Yongming, Lu Yong, Liu Gaochuan, Zhang Guobao. 2024: Seismic events classification based on Gram’s angle field and multi-scale residual neural network. Acta Seismologica Sinica, 46(1): 69-80. DOI: 10.11939/jass.20220144
    Liu Tao, Dai Zhijun, Chen Su, Fu Lei. 2022: Earthquake magnitude classification based on deep learning. Acta Seismologica Sinica, 44(4): 656-664. DOI: 10.11939/jass.20210046
    Chen Tian, Yi Yuanyuan. 2021: Random noise suppression of seismic data based on deep convolution neural network. Acta Seismologica Sinica, 43(4): 474-482. DOI: 10.11939/jass.20200135
    Liu Chao, Lei Qiyun, Yu Sihan, Yang Shun, Wang Yin. 2021: Using UAV photogrammetry technology to extract the quantitative parameters of earthquake surface rupture zone：A case study of the southern Zhongwei M7½ earthquake in 1709. Acta Seismologica Sinica, 43(1): 113-123. DOI: 10.11939/jass.20200039
    Cheng Xianqiong, Jiang Kezhi. 2021: Inversion of crustal thickness in Chinese mainland based on deep denoising autoencoder neural network. Acta Seismologica Sinica, 43(1): 34-47. DOI: 10.11939/jass.20200043
    Du Yankai, Gong Lixia, Li Qiang, Zhang Jingfa. 2020: Earthquake damage building identification technology based on high resolution remote sensing image with optimal segmentation. Acta Seismologica Sinica, 42(6): 760-768. DOI: 10.11939/jass.20200027
    Wei Yonggang, Yang Qianli, Wang Tingting, Jiang Changsheng, Bian Yinju. 2019: Earthquake and explosion identification based on Deep Learning residual network model. Acta Seismologica Sinica, 41(5): 646-657. DOI: 10.11939/jass.20190030
    Jing Shuaijun, Shuai Xianghua, Zhen Meng. 2019: Fine information extraction of 3D building seismic damage based on unmanned aerial vehicle oblique images. Acta Seismologica Sinica, 41(3): 366-376. DOI: 10.11939/jass.20180114
    Liang Jianwen, Chen Jianqi, Ba Zhenning. 2013: 3D scattering of obliquely incident SH waves by a cylindrical cavity in layered elastic half-space (Ⅱ): Numerical results and analysis. Acta Seismologica Sinica, 35(2): 173-183. DOI: 10.3969/j.issn.0253-3782.2013.02.004

Catalog

    Get Citation
    PDF
    XML
    Article views (341) PDF downloads (127) Cited by()
    Turn off MathJax
    Article Contents
    Abstract
    References
    Related Articles

    Supported by: Beijing Renhe Information Technology Co., Ltd.   

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return