предыдущий|следующий

A review and prospective research of earthquake damage assessment and remote sensing

  • role： First author第一作者
  • Affiliation：

    Institute of Engineering Mechanics, CEA, Harbin 150080, China

    Tianjin Earthquake Agency, Tianjin 300201, China

  • Email：anliqiang@sina.com
  • Introduction：安立强，研究方向为防震减灾技术及遥感的理论和应用。E-mail： anliqiang@sina.com
AN Liqiang12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Emergency Satellite Engineering and Application,Ministry of Emergency Management, Beijing 100124, China

  • Email：zhangjingfa@hotmail.com
  • Introduction：张景发，研究方向为遥感特征信息提取与定量遥感技术。E-mail： zhangjingfa@hotmail.com
ZHANG Jingfa3*,  
  • Affiliation：

    University School for Advanced Studies IUSS Pavia, Pavia 27100, Italy

RICARDO Monteiro4,  
  • Affiliation：

    Tianjin Earthquake Agency, Tianjin 300201, China

ZHANG Lei2

реферат

Earthquake loss assessment is an important part of earthquake emergency preparedness, emergency response, and reconstruction. With the increased awareness of earthquake risk and the increasing demand for earthquake protection and disaster reduction, earthquake disaster loss assessment technology has undergone rapid development in recent years. Moreover, as remote sensing technology has entered the era of big data, remote sensing data are beginning to be widely used in earthquake loss assessment.This study reviews and summarizes the development of earthquake loss assessment and its use in remote sensing techniques. In particular, we initially reviewed the development of earthquake loss assessments and compared and analyzed the differences between loss calculation methods and the main functions of earthquake loss assessment software platforms at home and abroad. Second, we summarize the calculation methods for estimating casualties, injuries, and economic loss according to whether structural damage is considered. Third, we summarize the development of emergency earthquake loss assessment methods and earthquake loss prediction methods based on remote sensing. Finally, we analyzed the application prospects of NTL remote sensing data as a spatialization tool for population and GDP data for earthquake disaster loss.The following conclusions can be drawn. (1) In recent years, the calculation granularity of the domestic earthquake disaster loss estimation system has gradually improved. The applied structural damage estimation method has changed from the traditional empirical earthquake damage matrix to the fragile curve, and the system application scenario has developed from the post-earthquake period to full-time application. (2) A seismic loss assessment based on macro-data and historical earthquake cases is easy to calculate, but its reusability needs to be improved. Comparatively, various sophisticated methods for building loss information involve logical reasoning, but they are restricted by varying degrees of data completeness. (3) Post-earthquake loss assessment and earthquake damage prediction technologies based on remote sensing have gradually improved. The current development trends include methods for acquiring multisource remote sensing data and intelligent remote sensing data analysis. However, current earthquake loss assessment work is limited by incomplete data, which hinders the promotion of new methods, the lack of a unified national business platform, and the lack of uncertainty.The following suggestions are proposed(1) further develop the role of remote sensing data in the entire process of earthquake disaster loss estimation; (2) build a professional, high-quality, and national unified earthquake disaster risk management platform; and (3) enrich and develop the reporting mechanism of earthquake disaster assessment results from the perspective of the identified audience.

ключеви́че слова́

earthquake damage estimation;earthquake risk assessment;loss assessment;earthquake damage assessment;remote sensing data

References

  1. 1.
    Abo El Ezz A, Smirnoff A, Nastev M, Nollet M J and McGrath H. 2019. ER2-Earthquake: interactive web-application for urban seismic risk assessment. International Journal of Disaster Risk Reduction, 34: 326-336
  2. 2.
    An J W, Xu J H, Nie G Z and Bai X F. 2015. Earthquake disaster rapid assessment for emergency response supported by high-precision data of hazard bearing body. Seismology and Geology, 37(4): 1225-1241
  3. 3.
    An L Q and Zhang J F. 2011. Application situation and trend of remote sensing technology used in earthquake disaster survey. Journal of Earthquake Engineering and Engineering Vibration, 31(2): 182-188
  4. 4.
    Badrinarayanan V, Kendall A and Cipolla R. 2017. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12): 2481-2495
  5. 5.
    Bai Y B, Mas E and Koshimura S. 2018. Towards operational satellite-based damage-mapping using U-Net convolutional network: a case study of 2011 Tohoku earthquake-tsunami. Remote Sensing, 10(10): 1626
  6. 6.
    Borzi B, Dell’Acqua F, Faravelli M, Gamba P, Lisini G, Onida M and Polli D. 2011. Vulnerability study on a large industrial area using satellite remotely sensed images. Bulletin of Earthquake Engineering, 9(2): 675-690
  7. 7.
    Cai Y J, Lin J Q, Liu J L, Chen Y, Huang X K and Lin Q L. 2015. Study on the rapid evaluation method for direct economic loss of earthquake based on Bayesian model. Earthquake Engineering and Engineering Dynamics, 35(2): 144-150
  8. 8.
    Cardona O D, Ordaz M G, Reinoso E, Yamín L E and Barbat A H. 2012. CAPRA-Comprehensive Approach to Probabilistic Risk Assessment: international initiative for risk management effectiveness//15th World Conference on Earthquake Engineering. Lisboa: [s.n.]: 1-10
  9. 9.
    Chen D D. 2020. Research on Extraction Method of Road Damage Information from High-Resolution Remote Sensing Image Based on Deeplabv3+. Beijing: Institute of Earthquake Forecasting, China Earthquake Administration
  10. 10.
    Chen F. 2016. Research on Evaluation Method of Seismic Casualties and Program Implementation. Xi’an: Xi’an University of Architecture and Technology
  11. 11.
    Chen H F. 2012. General Development and Design of HAZ-China Arthquake Disaster Loss Estimation System. Harbin: Institute of Engineering Mechanics, China Earthquake Administration
  12. 12.
    Chen H F, Sun B T and Chen X Z. 2014. GIS-based earthquake database information management system. Journal of Seismological Research, 37(4): 648-653
  13. 13.
    Chen K H, Wang L X, Liu Z and Wu H P. 2017. Design, development and implementation of the modular urban earthquake disaster risk assessment system. Technology for Earthquake Disaster Prevention, 12(4): 947-955
  14. 14.
    Chen L C, Papandreou G, Kokkinos I, Murphy K and Yuille A L. 2018a. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4): 834-848
  15. 15.
    Chen L C, Papandreou G, Schroff F and Adam H. 2017. Rethinking Atrous Convolution for Semantic Image Segmentation.
  16. 16.
    Chen L C, Zhu Y K, Papandreou G, Schroff F and Adam H. 2018b. Encoder-decoder with atrous separable convolution for semantic image segmentation//15th European Conference on Computer Vision. Munich: Springer: 833-851
  17. 17.
    Chen Q F and Chen L. 1997. Earthquake loss assessment based on GDP and population data. Acta Seismologica Sinica, 19(6): 640-649
  18. 18.
    Chen W K, He S L, Zhang J F, Zhou Z H and Guo X N. 2008. A review on methods of extracting earthquake damage information using remote sensing technology. Northwestern Seismological Journal, 30(1): 88-93
  19. 19.
    Chen X N, Zhang L X, Lin X C and Lu X Z. 2021. Study on matching algorithm of urban seismic scenarios based on ground motion database. Earthquake Engineering and Engineering Dynamics, 41(2): 76-83
  20. 20.
    Chen Y. 2015. Study on Rapid Assessment Method for Destructive Earthquake Casualty. Harbin: Institute of Engineering Mechanics, China Earthquake Administration
  21. 21.
    Chen Y, Lin J Q, Liu J L, Cai Y J, Huang X K and Lin Q L. 2017. Study on rapid assessment method of earthquake direct economic loss. World Earthquake Engineering, 33(1): 188-193
  22. 22.
    Cheng J Y and Yang Z. 1993. Regression analysis of mortality and the ratio of collapsing houses by Tangshan earthquake. Seismology and Geology, 15(1): 82-87
  23. 23.
    Cheng J Y and Yang Z. 1996. A software system of evaluating earthquake casualties. Seismology and Geology, 18(4): 462-470
  24. 24.
    Cheng X M, Shen Z F, Xing T Y and Dong W. 2016. Damaged building extraction and rapid assessment for earthquake disasters based on high-resolution remote sensing images. Journal of Natural Disasters, 25(3): 22-31
  25. 25.
    China Earthquake Administration. 2018. DB/T 79-2018 Earthquake disaster assessment based on remote sensing—Earthquake-caused direct economic loss. Beijing: Standards Press of China
  26. 26.
    Cui L P and Wang X Q. 2016. A survey on the earthquake induced building damage detection using SAR imagery. Technology for Earthquake Disaster Prevention, 11(2): 239-250
  27. 27.
    Dell’Acqua F, Gamba P and Jaiswal K. 2013. Spatial aspects of building and population exposure data and their implications for global earthquake exposure modeling. Natural Hazards, 68(3): 1291-1309
  28. 28.
    Dhu T, Robinson D, Clark D, Gray D and Row P. 2008. Event-based earthquake risk modelling//The 14th World Conference on Earthquake Engineering. Beijing: [s.n.]
  29. 29.
    Ding B Y. 1991. Two methods of earthquake Damace loss evaluation. Journal of Catastrophology, 6(2): 45-47
  30. 30.
    Ding X, Wang X Q, Dou A X, Yuan X X and Ding L. 2019. Design and realization of earthquake loss prediction system in nationwide scale based on grid data. Earthquake Research in China, 35(2): 238-247
  31. 31.
    Ding X, Wang X Q and Jiang L X. 1998. Computer-aided system for earthquake disaster loss estimation. Earthquake, 18(3): 286-292
  32. 32.
    Ding X, Wang X Q, Wang L and Zheng Y H. 2011. The development and application of catastrophe earthquake risk estimation system. Technology for Earthquake Disaster Prevention, 6(4): 454-460
  33. 33.
    Dong Y S, Chen H P, Pan Y Z and Fang W H. 2014. A rapid method for extracting building rubble based on post-earthquake high-resolution satellite imagery. Journal of Basic Science and Engineering, 22(6): 1079-1088
  34. 34.
    Fan Y D, Wu W, Wang W, Liu M and Wen Q. 2016. Research progress of disaster remote sensing in China. Journal of Remote Sensing (in Chinese), 20(5): 1170-1184
  35. 35.
    Fan Y D, Yang S Q, Wang L, Wang W, Nie J and Zhang B J. 2008. Study on urgent monitoring and assessment in Wenchuan earthquake. Journal of Remote Sensing, 12(6): 858-864
  36. 36.
    Federal Emergency Management Agency (FEMA) and National Institute of Building Sciences (NIBS). 1997. Earthquake Loss Estimation Methodology. HAZUS97. Federal Emergency Management Agency
  37. 37.
    Fu Z X and Li G P. 1993. Study on earthquake caused loss of life. Beijing: Seismological Press
  38. 38.
    Fu Z Y and Chen Q F. 2019. Earthquake disaster risk assessment based on nighttime lighting data: a case study of Yinchuan City//Urban Planning Society of China. Proceedings of the 2019 China Urban Planning Annual Conference 01 Urban Safety and Disaster Prevention Planning. Beijing: China Architecture and Building Press: 333-345
  39. 39.
    Future Earthquake Loss Prediction Research Group of the Earthquake Hazard Prevention Department of the National China Earthquake Administration. 1990. Research on Earthquake Loss Prediction in China. Beijing: Seismological Press (国家地震局震害防御司未来地震灾害损失预测研究组. 1990. 中国地震灾害损失预测研究. 北京: 地震出版社)
  40. 40.
    Gao H Y and Li Q X. 2010. Study on the rapid evaluation model for seismic casualties. Journal of Catastrophology, 25(S1): 275-277
  41. 41.
    Geiß C, Aravena Pelizari P, Marconcini M, Sengara W, Edwards M, Lakes T and Taubenböck H. 2015. Estimation of seismic building structural types using multi-sensor remote sensing and machine learning techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 104: 175-188
  42. 42.
    General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China and Standardization Administration of the People's Republic of China. 2008. GB/T 18207.1-2008 Terminology of protecting against and mitigating earthquake disasters—Part 1: basic terms. Beijing: Standards Press of China
  43. 43.
    General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China and Standardization Administration of the People's Republic of China. 2012. GB/T 18208.4-2011 Post-earthquake field works—Part 4: assessment of direct loss. Beijing: Standards Press of China
  44. 44.
    General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China and Standardization Administration of the People's Republic of China. 2015. GB/T 19428-2014 Code for earthquake loss estimation and its information management system. Beijing: Standards Press of China
  45. 45.
    Gong L X, Li Q, Zhang J F, Zeng Q M, Liu M Z and Li C L. 2013. Object-oriented detection of earthquake building damages. Earthquake, 33(2): 109-114
  46. 46.
    Guo J L. 2003. The prediction of the economic loss and casualty due to earthquake. Earthquake Research in Shanxi, 2: 4-6, 16
  47. 47.
    Guo Z L, Liu M G and Xie D C. 2004. Genetic algorithm-neural network-based economic losses assessment of seismic disaster. Journal of Natural Disasters, 13(6): 92-96
  48. 48.
    Hancilar U, Tuzun C, Yenidogan C and Erdik M. 2010. ELER software-a new tool for urban earthquake loss assessment. Natural Hazards and Earth System Sciences, 10(12): 2677-2696
  49. 49.
    He H X, Wu B, Li R and Han X Z. 2018. Research on requirements and strategies of satellite cooperative observation for natural disaster emergency response. Spacecraft Recovery and Remote Sensing, 39(6): 91-101
  50. 50.
    He K M, Gkioxari G, Dollár P and Girshick R. 2017. Mask R-CNN//2017 IEEE International Conference on Computer Vision (ICCV). Venice: IEEE: 2980-2988
  51. 51.
    Hu S Q. 2007. The Research on Earthquake Damage Prediction of Building Stock and the Discussion of Direct Economic Loss of Infrastructure. Harbin: Institute of Engineering Mechanics, China Earthquake Administration
  52. 52.
    Huang P D. 2018. Study on the Influencing Factors of Casualties Caused by Earthquakes Abroad. Harbin: Institute of Engineering Mechanics, China Earthquake Administration
  53. 53.
    Huang X and Sun M. 2016. The rapid assessment of wounded personnel based on RBF neural network model under the background earthquake disaster. Systems Engineering, 34(8): 129-135
  54. 54.
    Institute of Earthquake Forecasting, China Earthquake Administration and Center for Earth Observation and Digital Earth Chinese Academy of Sciences. 2010. Atlas of Remote Sensing Interpretation of Building Damage in Wenchuan Earthquake. Beijing: Seismological Press (中国地震局地震预测研究所, 中国科学院对地观测与数字地球科学中心. 2010. 汶川地震建筑物震害遥感解译图集. 北京: 地震出版社)
  55. 55.
    Ji M, Liu L F and Buchroithner M. 2018. Identifying collapsed buildings using post-earthquake satellite imagery and convolutional neural networks: a case study of the 2010 Haiti earthquake. Remote Sensing, 10(11): 1689
  56. 56.
    Jia H X. 2020. Research of Seismic Casualties Assessment Model Based on Machine Learning Algorithm. Harbin: Institute of Engineering Mechanics, China Earthquake Administration
  57. 57.
    Jiang L X, Shuai X H, Zhang J F and Li Z Q. 2003. Study of earthquake emergency command system. Earthquake, 23(2): 115-120
  58. 58.
    Karaman H, Şahin M and Elnashai A S. 2008. Earthquake loss assessment features of Maeviz-Istanbul (Hazturk). Journal of Earthquake Engineering, 12(S2): 175-186
  59. 59.
    Li D J, Guo H T, Lu J, Zhao C, Lin Y Z and Yu D H. 2020. A remote sensing image classification procedure based on multilevel attention fusion U-Net. Acta Geodaetica et Cartographica Sinica, 49(8): 1051-1064
  60. 60.
    Li L. 2009. Study on Earthquake Damage Prediction of Urban Buildings and Earthquake Economic Losses. Dalian: Dalian University of Technology
  61. 61.
    Li Q. 2018. Study on Key Technology of Earthquake Emergency Using Multi-Mode Remote Sensing Data. Harbin: Institute of Engineering Mechanics, China Earthquake Administration
  62. 62.
    Li S J and Liu M. 2018. Application of satellite remote sensing in emergency monitoring of major earthquake disaster in China. City and Disaster Reduction, (4): 7-10
  63. 63.
    Li S Z, Jia X Y and Zhu Y L. 1995. Earthquake damage evaluation software EDEP-93 and its use in puer earthquake. Journal of Natural Disasters, 4(1): 39-46
  64. 64.
    Li S Z and Yin Z Q. 1993. Evaluation of earthquake and database system. Earthquake Research in China, 9(3): 74-85
  65. 65.
    Li Y W and Yu S Z. 2021. Study on the assessment method of earthquake casualties in the eastern region. World Earthquake Engineering, 37(3): 207-213
  66. 66.
    Li Y Y, Chen J G, Zhang X L and Yuan H Y. 2015. Structure destruction based earthquake casualty estimates. Journal of Tsinghua University (Science and Technology), 55(7): 803-807, 814
  67. 67.
    Lin J. 2011. Research trends of Earthquake disaster mitigation in USA and Japan. Overview of Disaster Prevention, (1): 32-35
  68. 68.
    Lin X C. 2017. Numerical simulation and risk control of earthquake disasters in urban building complexes. City and Disaster Reduction, (3): 18-22
  69. 69.
    Liu B C. 1981. Earthquake Risk and Damage Functions: Application to New Madrid. Boulder: Westview Press
  70. 70.
    Liu H X. 1988. Development of earthquake engineering in China-review and outlook. Earthquake Engineering and Engineering Vibration, 8(2): 1-7
  71. 71.
    Liu J L and Lin J Q. 2012. Study on assessment method for earthquake casualty based on epicentral intensity. Journal of Natural Disasters, 21(5): 113-119
  72. 72.
    Liu R S, Yu S Z, Yan D Q, He M L, Yue X and Li J. 2014. Refined method for rapid assessment of seismic damage and economic loss. Journal of Basic Science and Engineering, 22(5): 928-940
  73. 73.
    Liu W X, Shu Y Z, Tang X M and Liu J M. 2020. Remote sensing image segmentation using dual attention mechanism Deeplabv3+ algorithm. Tropical Geography, 40(2): 303-313
  74. 74.
    Liu X H and Chen Y P. 1985. A review of urban earthquake disaster prevention technologies abroad. World Earthquake Engineering, 1(2): 10-17
  75. 75.
    Liu X H, Li H and He J. 1985. A framework for seismic losses estimation. Earthquake Engineering and Engineering Vibration, 5(4): 1-12
  76. 76.
    Liu Y H. 2020. Extraction and Research on Building Information from High-Resolution Remote Sensing Image for Seismic Risk Assessment. Beijing: Institute of Geology, China Earthquake Administration
  77. 77.
    Liu Y Q, Cai H C, Zhao J and Feng S M. 1998. Prediction of economic losses caused by seismic hazard in northern part of Hebei province. North China Earthguake Sciences, 16(4): 39-50
  78. 78.
    Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE: 3431-3440
  79. 79.
    Lu X Z, Zeng X, Xu Z, et al. 2018, Improving the accuracy of near-real-time seismic loss estimation using post-earthquake remote sensing images, Earthquake Spectra, 34(3): 1219-1245.
  80. 80.
    Lu X Z, Tian Y, Xu Z and Xiong C. 2020. A new urban earthquake disaster simulation method: city-scale nonlinear time-history analysis. Chinese Science Bulletin, 65(11): 1055-1062
  81. 81.
    Ma J J, Feng Q M and Zhou H Y. 2013. Method for predicting earthquake damage of housing building groups based on image explanation. Journal of Natural Disasters, 22(3): 62-67
  82. 82.
    Ma Y H and Xie L L. 2000. Methodologies for assessment of earthquake casualty. Earthquake Engineering and Engineering Vibration, 20(4): 140-147
  83. 83.
    Ma Y J, Li Y Y and Han X L. 2007. Earthquake economic loss estimation based on the ANN. World Earthquake Engineering, 23(1): 146-150
  84. 84.
    Matsuoka M, Mito S, Midorikawa S, Miura H, Quiroz L G, Maruyama Y and Estrada M. 2014. Development of building inventory data and earthquake damage estimation in Lima, Peru for future earthquakes. Journal of Disaster Research, 9(6): 1032-1041
  85. 85.
    Matsuoka M, Miura H, Midorikawa S and Estrada M. 2013. Extraction of urban information for seismic hazard and risk assessment in Lima, Peru using satellite imagery. Journal of Disaster Research, 8(2): 328-345
  86. 86.
    McLaren T M, Myers J D, Lee J S, Tolbert N L, Hampton S D and Navarro C M. 2008. MAEviz: an earthquake risk assessment system//Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Irvine: ACM: 88 [DOI: 10.1145/1463434.1463534]
  87. 87.
    Mitomi H, Yamazaki F and Matsuoka M. 2001. Development of automated extraction method for building damage area based on maximum likelihood classifier//8th International Conference on Structural Safety and Reliability. Newport Beach: [s.n.]
  88. 88.
    Mitomi Y and Takeuchi S. 1995. Analysis of spectral feature of the damaged areas by liquefaction and fire using airborne MSS data//Proceedings of the 18th Japanese Conference on Remote Sensing. [s.l.]: [s.n.]
  89. 89.
    Molina S, Lang D H and Lindholm C D. 2010. SELENA-An open-source tool for seismic risk and loss assessment using a logic tree computation procedure. Computers and Geosciences, 36(3): 257-269
  90. 90.
    OOFIMS. 2020. Object-Oriented Framework for Infrastructure Modeling and Simulation[EB/OL]. [2021-10-30].
  91. 91.
    Pittore M and Wieland M. 2013. Toward a rapid probabilistic seismic vulnerability assessment using satellite and ground-based remote sensing. Natural Hazards, 68(1): 115-145
  92. 92.
    Polli D and Dell’Acqua F. 2011. Fusion of optical and SAR data for seismic vulnerability mapping of buildings//Prasad S, Bruce L M and Chanussot J, eds. Optical Remote Sensing: Advances in Signal Processing and Exploitation Techniques. Berlin, Heidelberg: Springer: 329-341
  93. 93.
    Polli D, Dell’Acqua F and Gamba P. 2009. First steps towards a framework for earth observation (EO)-based seismic vulnerability evaluation. Environmental Semeiotics, 2(1): 16-30
  94. 94.
    Qi W H, Su G W, Sun L, Yang F and Wu Y. 2017. “Internet+” approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth: a case study in Tangshan, China. Natural Hazards, 86(S1): 107-139
  95. 95.
    Qian F L and Cui J. 2013. Application of BP neural network analysis in forecasting emergency demand——A case study on earthquake casualty forecasting. China Safety Science Journal, 23(4): 20-25
  96. 96.
    Riedel I, Guéguen P, Dalla Mura M, Pathier E, Leduc T and Chanussot J. 2015. Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods. Natural Hazards, 76(2): 1111-1141
  97. 97.
    Riedel I, Guéguen P, Dalla Mura M, Pathier E, Leduc T and Chanussot J. 2015. Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods. Natural Hazards, 76(2): 1111-1141
  98. 98.
    Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//18th International Conference on Medical Image Computing and Computer Assisted Intervention. Munich: Springer: 234-241
  99. 99.
    Schäfer D, Pietsch M and Wenzel H. 2013. EQvis: a consequence based risk management software tool//Zingoni A, ed. Research and Applications in Structural Engineering, Mechanics and Computation. London: CRC Press
  100. 100.
    Shi W H, Chen K H, Xie Y Q, Li X, Pang W D and Dai B Y. 2012. Prediction method research on casualties due to earthquake disaster in Yunnan. Journal of Seismological Research, 35(3): 387-392
  101. 101.
    Silva V, Crowley H, Pagani M, Monelli D and Pinho R. 2014. Development of the OpenQuake engine, the Global Earthquake Model’s open-source software for seismic risk assessment. Natural Hazards, 72(3): 1409-1427
  102. 102.
    Strasser F O, Bommer J J, Şeşetyan K, Erdik M, Çağnan Z, Irizarry J, Goula X, Lucantoni A, Sabetta F, Bal I E, Crowley H and Lindholm C. 2008. A comparative study of European earthquake loss estimation tools for a scenario in Istanbul. Journal of Earthquake Engineering, 12(S2): 246-256
  103. 103.
    Sun B T and Li Y. 2021. Evolution and development of earthquake disaster prediction in China. Engineering Mechanics, 38(1): 1-7, 51
  104. 104.
    Sun Z, Guo J X and Zhao X. 2019. Design and implementation of automatic system of earthquake emergency disaster information pre-evaluation based on kilometer grid. Information and Communications, (7): 74-76
  105. 105.
    Tian L L. 2012. Analysis of the influencing factors of casualties in earthquake disasters and the formulas for estimating casualties. Beijing: Capital University of Economics and Business
  106. 106.
    Tian W F. 2015. Research on Recognition of Earthquake Damaged Road Based on High Resolution Remote Sensing Imagery—Taking Ludian Earthquake for an Example. Beijing: Beijing Jiaotong University
  107. 107.
    Turker M and San B T. 2004. Detection of collapsed buildings caused by the 1999 Izmit, Turkey earthquake through digital analysis of post-event aerial photographs. International Journal of Remote Sensing, 25(21): 4701-4714
  108. 108.
    Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser Ł and Polosukhin I. 2017. Attention is all you need//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc.: 6000-6010
  109. 109.
    Vetrivel A, Gerke M, Kerle N, Nex F and 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. ISPRS Journal of Photogrammetry and Remote Sensing, 140: 45-59
  110. 110.
    Wang D L. 2007. Quantitative study on the relationship between remote sensing seismic damage index and ground survey seismic damage index. Beijing: Institute of Earthquake Forecasting, China Earthquake Administration (王栋梁. 2007. 遥感震害指数与地面调查震害指数关系的定量研究. 北京: 中国地震局地震预测研究所)
  111. 111.
    Wang H F, Zhai Y M and Chen X. 2010. Rapid prediction of earthquake disaster of buildings based on remote sensing images. North China Earthquake Sciences, 28(2): 45-47
  112. 112.
    Wang L. 2007. Research and implementation of earthquake disaster loss assessment method based on remote sensing images. Beijing: Institute of Earthquake Forecasting, China Earthquake Administration (王龙. 2007. 基于遥感影像的地震灾害损失评估方法研究与实现. 北京: 中国地震局地震预测研究所)
  113. 113.
    Wang L, Dou A X, Wang X Q, Dong Y F, Ding X, Li Z, Yuan X X and Qiu Y R. 2011. Damage assessment of Haiti earthquake emergency using high resolution remote sensing imagery//Proceedings of SPIE 8203, Remote Sensing of the Environment: The 17th China Conference on Remote Sensing. Hangzhou: SPIE: 820310 [DOI: 10.1117/12.910416]
  114. 114.
    Wang W Z. 2012. Earthquake-Caused Direct Economic Loss Assessment: BP Neural Network and Its Application. Chengdu: Southwestern University of Finance and Economics
  115. 115.
    Wang X Q and Ding X. 2004. Estimation system of disastrous losses in seismic site based on GIS. Journal of Natural Disasters, 13(1): 118-125
  116. 116.
    Wang X Q and Ding X. 2019.Forecasting researches on disaster loss of Chinese mainland during 2016 to 2025.2016~2025 Forecasting Researches on Earthquake Risk Region and Disaster Loss of Chinese Mainland During 2016 to 2025. Beijing: Sinomap Press
  117. 117.
    Wang X Q, Ding X, Wang L and Wang Y. 2009a. A study on fast earthquake loss assessment and its application to 2008 Wenchuan M8 earthquake. Acta Seismologica Sinica, 31(2): 205-211
  118. 118.
    Wang X Q, Dou A X, Ding X and Li Y W. 2015a. Advance on the RS-based emergency seismic intensity assessment. Journal of Geo-Information Science, 17(12): 1536-1544
  119. 119.
    Wang X Q, Dou A X, Sun G Q, Ding X, Wang L and Yuan X X. 2013. Intensity assessment of the 2010 Yushu MS7.1 earthquake based on synthetic seismic damage index. Earthquake, 33(2): 1-10
  120. 120.
    Wang X Q, Dou A X, Wang L, Yuan X X, Ding X and Zhang W. 2015b. RS-based assessment of seismic intensity of the 2013 Lushan, Sichuan, China MS7.0 earthquake. Chinese Journal of Geophysics, 58(1): 163-171
  121. 121.
    Wang X Q, Fu Z X, Ding X and Xu Y J. 2000. The algorithm principle and main functions of earthquake disaster and loss prediction system. Earthquake, 20(S1): 222-226
  122. 122.
    Wang X Q, Wang L, Zhang X H, Ding X, Qiu H, Dong Y F, Dou A X, Zhang F Y and Liu Z T. 2009b. Primary quantitative study on earthquake damage extracted from remote sensing imagery——A case study of Doujiangyan due to the Wenchuan M8.0 earthquake. Earthquake, 29(1): 174-181
  123. 123.
    Wang X Q, Wei C J, Miao C G, Zhang J F, Shan X J and Ma Q Z. 2003. The extraction of seismic damage from remote sensing images——a case study of Bachu-Jiashi earthquake with MS=6.8 occurred on Feb. 24, 2003. Earth Science Frontiers, 10(S1): 285-291
  124. 124.
    Wei C J, Zhang Y Z, Zhu B Q, Zhang Z K, Yu T X and Gao Z M. 1996. Application of remote sensing and GIS techniques in producing earthquake isoseismal. Journal of Natural Disasters, 5(3): 18-28
  125. 125.
    Wen R Z. 1997. The constitution of GIS-based urban earthquake resistance system. World Information on Earthquake Engineering, 13(4): 28-33
  126. 126.
    Wieland M, Pittore M, Parolai S, Begaliev U, Yasunov P, Tyagunov S, Moldobekov B, Saidiy S, Ilyasov I and Abakanov T. 2015. A multiscale exposure model for seismic risk assessment in Central Asia. Seismological Research Letters, 86(1): 210-222
  127. 127.
    Wu H J, Feng T N, Hong Z H, Ai Z S, Wang C X and Jiang C H. 2013. Earthquake casualty prediction model based on remote sensing images. Journal of Tongji University (Medical Science), 34(5): 36-39
  128. 128.
    Wu H J, Guo M Z and Zhang J. 2006. Application of remote sensing technology in the field of earthquake prevention and disaster reduction. Earthquake Engineering and Engineering Vibration, 26(3): 267-269
  129. 129.
    Xiao G X. 1986. Economic problems in earthquake engineering. World Earthquake Engineering, 2(1): 3-10, 19
  130. 130.
    Xiao G X. 1987. A prediction method for earthquake loss. Journal of Seismology, (1): 1-8
  131. 131.
    Xiao G X. 1991. Rapid assessment of disaster losses in post-earthquake. Journal of Catastrophology, 6(4): 12-17
  132. 132.
    Xie J Z, Che S F and Lin Y. 2017. Earthquake disaster economic loss estimation and prediction based on Neural Networks with random weights. Journal of Catastrophology, 32(1): 1-4, 10
  133. 133.
    Xie L L and Yin Z Q. 1996. Research on earthquake damage assessment technology for future earthquakes in Chinese mainland//Proceedings of the Second Earthquake Symposium Across the Taiwan Straits
  134. 134.
    Xu J H, An J W and Nie G Z. 2016. Development of earthquake emergency disaster information pre-evaluation data based on km grid. Seismology and Geology, 38(3): 760-772
  135. 135.
    Xue T F. 2017. Earthquake Damaged Buildings Extraction from SAR Images Using Change Detection Technology Based on Multiple Features. Harbin: Institute of Engineering Mechanics, China Earthquake Administration (薛腾飞. 2017. 基于SAR多特征变化检测的震害建筑物提取研究. 哈尔滨: 中国地震局工程力学研究所)
  136. 136.
    Xue T F, Zhang J F and Li Q. 2016. Extraction of earthquake damage buildings from multi-source remote sensing data based on correlation change detection and object-oriented classification techniques. Acta Seismologica Sinica, 38(3): 496-505
  137. 137.
    Yamazaki F, Suzuki D and Maruyama Y. 2008. Use of digital aerial images to detect damages due to earthquakes//The 14th World Conference on Earthquake Engineering. Beijing: [s.n.]
  138. 138.
    Yang F, Zheng B Z and Yan L L. 2009. System of earthquake casualty assessment based on BP neural network. Technology for Earthquake Disaster Prevention, 4(4): 428-435
  139. 139.
    Ye X, Qin Q M, Wang J, Zheng X P and Wang J H. 2019. Detecting damaged buildings caused by earthquake from remote sensing image using local spatial statistics method. Geomatics and Information Science of Wuhan Unive, 44(1): 125-131
  140. 140.
    Yin Z Q. 1991. A study for predicting earthquake disaster loss. Earthquake Engineering and Engineering Vibration, 11(4): 87-96
  141. 141.
    Yin Z Q. 1995. Methods for Predicting Seismic Hazard and Loss. Beijing: Seismological Press
  142. 142.
    Yu S, Wang H X and Ma Y J. 2005. Three-layer BP network model for estimation of casualties in an earthquake. Earthquake Engineering and Engineering Vibration, 25(6): 113-117
  143. 143.
    Yuan Y. 2018. Study of Earthquake-Induced Building Damage Information Extraction by Using Change Detection of SAR Images Coherence. Langfang: Institute of Disaster Prevention
  144. 144.
    Zeng X, Liu S X, Xu Z and Lu X Z. 2016. Earthquake loss prediction for campus buildings based on FEMA-P58 method: a case study. Engineering Mechanics, 33(S1): 113-118
  145. 145.
    Zhai Y M. 2009. Research on Application of High Resolution Remote Sensing Image in Predicition and Rapid Evaluation of Urban Earthquake Damage. Shanghai: Tongji University
  146. 146.
    Zhang H, Qin K, Bi Q, Zhang Y and Xu K. 2021. Attention guided 3D ConvNet for aerial scene change detection. Journal of Applied Sciences, 39(2): 272-280
  147. 147.
    Zhang J F, Li Q, Zhang Q Y, Tian T, Shen W H, Xue T F and Wang J F. 2018. Intensity zone of the 1976 Ms7.8 Tangshan earthquake based on multi-source remote sensing images. Journal of Remote Sensing, 22(S1): 162-173
  148. 148.
    Zhang W, Wang X Q, Sun G Q, Ding X, Yuan X X and Guo J X. 2013. An improved change detection algorithm for building damage extraction based on high resolution RS images. Earthquake, 33(2): 87-95
  149. 149.
    Zhang Y, Wang X M and Zhao X Z. 2018. Study on community based rapid earthquake assessment system of Shanghai. Computer Programming Skills and Maintenance, (10): 15-17
  150. 150.
    Zhao H S, Shi J P, Qi X J, Wang X G and Jia J Y. 2017. Pyramid scene parsing network//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE: 6230-6239
  151. 151.
    Zhao Q, Zhai Y M and Li T Z. 2012. Study on application of high resolution remote sensing images in rapid prediction of earthquake disaster in urban area. Journal of Catastrophology, 27(2): 72-76
  152. 152.
    Zhao S D, Zhang N, Zhang S W and Sun X D. 2016. Research on rapid evaluation method of earthquake direct economic loss based on LM-BP neural network. Journal of Seismological Research, 39(3): 500-506
  153. 153.
    Zheng S S, Zhang R M, Chen F, Long L, Zhou Y and Zheng J. 2019. Research on theory and application of earthquake casualty estimates. World Earthquake Engineering, 35(1): 87-96
  154. 154.
    Zheng Y, He J C, Zheng S S, Ke L, Long L and Sun L F. 2020. Study on China earthquake disaster loss assessment system. Journal of Natural Disasters, 29(4): 34-42
  155. 155.
    Zhu Z Z, Lu J Y, Li M and Zhao Y. 2020. Building age estimation and seismic damage prediction based on remote sensing image and point density. Journal of Jiangsu University (Natural Science Edition), 41(4): 382-386, 404
  156. 156.
    Zou Q J, Mao G M, Sun Z K, Ma C Q and Ma G M. 1995. Vulnerability study on casualties due to earthquake. Journal of Natural Disasters, 4(3): 60-68

Читать полностью

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website