基于倾斜摄影技术的典型情景可视化震害定量评估--以四川马尔康震群为例

doi:10.3969/j.issn.0253-4967.2023.04.003

地震地质 ›› 2023, Vol. 45 ›› Issue (4): 847-863.DOI: 10.3969/j.issn.0253-4967.2023.04.003

基于倾斜摄影技术的典型情景可视化震害定量评估--以四川马尔康震群为例

肖本夫¹⁾(), 袁小祥²^,³⁾^,^*(), 陈波⁴⁾, 张露露¹⁾, 梁远玲¹⁾, 祁玉萍¹⁾, 杨璐遥¹⁾, 刘洋¹⁾

¹⁾ 四川省地震局, 成都 610041
²⁾ 中国地震局工程力学研究所, 中国地震局地震工程与工程振动重点实验室, 哈尔滨 150080
³⁾ 中国地震局地震预测研究所, 北京 100036
⁴⁾ 中国地震局地球物理研究所, 北京 100081

收稿日期:2022-08-26 修回日期:2023-01-18 出版日期:2023-08-20 发布日期:2023-09-20
通讯作者: ^*袁小祥, 男, 1983年生, 副研究员, 主要从事基于遥感的震害信息提取、仿真与风险评估等方面的研究, E-mail: yuanxx@ief.ac.cn。
作者简介:
肖本夫, 男, 1986年生, 2013年于中国地震局地震预测研究所获构造地质学专业硕士学位, 工程师, 主要从事地震应急、地震地质与数字地震学研究, E-mail: xiaobf_1986@163.com。
基金资助:
国家重点研发计划项目(2019YFC1509402); 中国地震局地震预测研究所基本科研业务专项(CEAIEF2022050505); 四川地震科技创新团队专项(201902); 四川省地震局地震科技专项(LY2211)

QUANTITATIVE ASSESSMENT OF SEISMIC DAMAGE IN TYPICAL SCENARIOS BASED ON OBLIQUE AERIAL PHOTOGRAPHY: THE CASE OF THE MAERKANG EARTHQUAKE SWARM, SICHUAN, CHINA

XIAO Ben-fu¹⁾(), YUAN Xiao-xiang²^,³⁾^,^*(), CHEN Bo⁴⁾, ZHANG Lu-lu¹⁾, LIANG Yuan-ling¹⁾, QI Yu-ping¹⁾, YANG Lu-yao¹⁾, LIU Yang¹⁾

¹⁾ Sichuan Earthquake Agency, Chengdu 610041, China
²⁾ Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
³⁾ Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
⁴⁾ Institute of Geophysics, China Earthquake Administration, Beijing 100081, China

Received:2022-08-26 Revised:2023-01-18 Online:2023-08-20 Published:2023-09-20

摘要/Abstract

摘要：

地震灾害定量评估是震后地震应急处置的重要流程之一, 评估结果的准确性直接影响应急处置和决策的工作效率。文中构建了基于倾斜摄影技术的典型情景可视化震害定量评估流程, 建立了情景可视化震害解译标志, 并以2022年6月10日四川马尔康6.0级震群为例提取了极震区典型情景的可视化房屋震害信息, 实现了对极震区地震灾害定量评估, 同时结合现场震害调查结果验证了所提出方法的可行性和准确性。结果表明: 1)基于倾斜摄影技术构建的典型情景可视化模型可反映建筑物的顶部、外墙和底部等部位的震害信息, 能够直观地反映震后地震的破坏情况, 与使用传统的准垂直视角震后遥感影像相比, 利用该模型可更加有效地提取建筑物承灾体的基础信息和震害信息; 2)按照等效震害指数对3类共计520栋房屋结构的地震破坏程度进行了定量评定, 其中, 藏式石木结构的等效震害指数为0.60, 砖混结构的等效震害指数为0.44, 钢筋混凝土框架结构的等效震害指数为0.37, 综合判定研究区的地震烈度为Ⅷ度(8度), 与现场调查结果一致; 3)结合现场房屋震害调查结果, 基于情景可视化震害提取得到的分类结果与现场调查震害分类结果的OA值和Kappa系数分别为92%和0.87, 基于无人机正射影像震害提取的分类结果与现场调查震害结果的OA值和Kappa系数分别为45%和0.25, 与基于无人机正射影像的震害定量评估方法相比, 基于情景可视化的震害定量评估方法在识别精度和准确度方面效果更好。基于倾斜摄影技术的典型情景可视化震害定量评估方法为将高精度无人机遥感数据用于建筑物震害定量评估工作提供了新的思路, 其结果可作为地震烈度评定、地震应急救援和人员指挥调度的参考依据。

关键词: 倾斜摄影技术, 情景可视化, 地震灾害定量评估, 四川马尔康

Abstract:

Post-earthquake disaster information extraction and quantitative evaluation are the foundations of earthquake relief work. The effectiveness of its transmission mode and the accuracy of evaluation results directly affect the efficiency of post-earthquake emergency analysis, emergency response decision-making, and earthquake relief. In recent years, the nationally targeted poverty alleviation, reinforcement of housing facilities in earthquake-prone areas, and the rapid promotion of urbanization have greatly improved the overall seismic protection capability of buildings, and the reference indexes of seismic damage assessment have changed from the previous focus on the damage indexes of the roof of the disaster-bearing body to the comprehensive damage indexes of the roof of the disaster-bearing body, external walls, and subsidiary structures. The traditional two-dimensional damage assessment method based on a quasi-vertical perspective can hardly meet the requirements of an accurate quantitative assessment of earthquake damage at this stage.

With the rapid development of aerial photography technology, the efficiency and accuracy of earthquake hazard information extraction and quantitative earthquake hazard assessment have been greatly improved. The three-dimensional seismic damage scenario visualization model based on UAV oblique photography technology has the advantages of multi-angle, high accuracy, and rich texture, which can accurately reflect the characteristic differences of seismic disasters of disaster-bearing bodies. It can be used to realize multi-dimensional and high-granularity seismic damage information extraction, thus effectively improving the accuracy of a quantitative assessment of single-time and space-time seismic damage, and can provide scientific and technological support for practical quantitative assessment of seismic damage.

On June 10, 2022, a M6.0 magnitude earthquake swarm occurred in Maerkang City, Ngawa Tibetan and Qiang Autonomous Prefecture(also known as Aba Prefecture), Sichuan Province, in which the epicenter locations of three earthquakes of M5.8, M6.0 and M5.2 were located in Caodeng Town, Maerkang City, Aba Prefecture, Sichuan Province, with source depths of 10km, 13km and 15km, respectively. After the swarm, the Sichuan earthquake agency initiated a level Ⅱ emergency response, and the earthquake site working group rushed to the earthquake site with UAV equipment to carry out oblique photography of typical buildings, geological hazards, and other earthquake damage.

In this study, the quantitative evaluation process of typical scenario visualization seismic damage based on oblique photography technology is constructed. Based on the texture, spectrum, shape, position, and combination of UAV remote sensing images, the interpretation signs of scenario visualization seismic damage are established. Taking the Maerkang 6.0 earthquake swarm in Sichuan on June 10, 2022 as an example, the seismic damage information of typical scenario visualization houses in the meizoseismal area is extracted, and the quantitative evaluation of seismic disaster in the meizoseismal area is realized. At the same time, the feasibility and accuracy of the method are verified by the field seismic damage investigation results. The results show that: 1)the typical scenario visualization model constructed based on oblique photography can reflect the earthquake damage information on the top, exterior walls and bottom of buildings, which can intuitively reflect the earthquake damage aftershocks. Compared with the traditional post-earthquake remote sensing images from a quasi-vertical perspective, this model can more effectively extract the basic information and seismic damage information of the building hazard-bearing body. 2)According to the equivalent seismic damage index, the seismic damage degree of 520 houses in 3 types of building structures is quantitatively evaluated. Among these the equivalent seismic damage index of a Tibetan stone-wood structure is 0.60, the equivalent seismic damage index of a brick-concrete structure is 0.44, and the equivalent seismic damage index of a reinforced concrete frame structure is 0.37. The seismic intensity of the study area is determined to be Ⅷ degree(8 degree), which is consistent with the field survey results. The OA values and Kappa coefficients of seismic damage extraction based on visualization and field survey were 92% and 0.87, respectively, while the OA values and Kappa coefficients of seismic damage extraction based on UAV orthophotograph and field survey were 45% and 0.25, respectively, which showed that the seismic damage extraction based on visualization was more accurate than that based on UAV orthophotograph in terms of recognition accuracy and precision. Compared with the quantitative assessment method of seismic damage based on UAV orthophotograph, the quantitative assessment method of earthquake damage based on scenario visualization is more effective in terms of recognition accuracy and precision. The typical scenario-based visualized seismic damage extraction method based on oblique photography technology provides a new idea for high-precision UAV remote sensing data for building damage extraction work, and its extraction results can be used as a reference basis for seismic intensity assessment, earthquake emergency rescue and personnel command and dispatch.

Key words: Oblique photography, scenario visualization, seismic damage, quantitative assessment, Maerkang

肖本夫, 袁小祥, 陈波, 张露露, 梁远玲, 祁玉萍, 杨璐遥, 刘洋. 基于倾斜摄影技术的典型情景可视化震害定量评估--以四川马尔康震群为例[J]. 地震地质, 2023, 45(4): 847-863.

XIAO Ben-fu, YUAN Xiao-xiang, CHEN Bo, ZHANG Lu-lu, LIANG Yuan-ling, QI Yu-ping, YANG Lu-yao, LIU Yang. QUANTITATIVE ASSESSMENT OF SEISMIC DAMAGE IN TYPICAL SCENARIOS BASED ON OBLIQUE AERIAL PHOTOGRAPHY: THE CASE OF THE MAERKANG EARTHQUAKE SWARM, SICHUAN, CHINA[J]. SEISMOLOGY AND GEOLOGY, 2023, 45(4): 847-863.

图/表 11

参考文献 38

[1]	杜浩国, 林旭川, 张建国, 等. 2021. 基于改进蚁群算法与无人机影像的震害识别方法及其在漾濞地震中的应用[J]. 地震地质, 43(4): 1013-1029. doi: 10.3969/j.issn.0253-4967.2021.04.018. DOI
	DU Hao-guo, LIN Xu-chuan, ZHANG Jian-guo, et al. 2021. A seismic damage identification method based on improved ant colony algorithm and unmanned aerial vehicle images and its application to Yangbi earthquake[J]. Seismology and Geology, 43(4): 1013-1029 (in Chinese).
[2]	范熙伟, 聂高众, 邓砚, 等. 2017. 基于摄影测量技术的房屋提取方法: 以中国西部地区乡村为例[J]. 地震地质, 39(4): 805-818. doi: 10.3969/j.issn.0253-4967.2017.04.014. DOI
	FAN Xi-wei, NIE Gao-zhong, DENG Yan, et al. 2017. The extraction of house distribution based on photogrammetry method: Taking the countryside in the west of China for example[J]. Seismology and Geology, 39(4): 805-818 (in Chinese).
[3]	国家市场监督管理总局, 国家标准化管理委员会. 2020. 中国地震烈度表(GB/T 17742-2020)[S]. 北京: 中国标准出版社.
	State Administration for Market Regulation, Inspection and Quarantine of the People's Republic of China, Standards Administration of China. 2020. The Chinese Seismic Intensity Scale(GB/T 17742-2020)[S]. Standards Press of China, Beijing(in Chinese) (in Chinese).
[4]	刘静, 陈涛, 张培震, 等. 2013. 机载激光雷达扫描揭示海原断裂带微地貌的精细结构[J]. 科学通报, 58(1): 41-45.
	LlU Jing, CHEN Tao, ZHANG Pei-zhen, et al. 2013. Illuminating the active Haiyuan fault, China by airborne light detection and ranging[J]. Chinese Science Bulletin, 58(1): 41-45 (in Chinese).
[5]	帅向华, 刘钦, 甄盟, 等. 2018. 倾斜摄影技术在云南鲁甸地震现场的应用研究[J]. 震灾防御技术, 13(1): 158-167.
	SHUAI Xiang-hua, LIU Qin, ZHEN Meng, et al. 2018. Application of the UVA oblique photography technique in the field of Ludian earthquake, Yunnan[J]. Technology for Earthquake Disaster Prevention, 13(1): 158-167 (in Chinese).
[6]	孙家抦. 2013. 遥感原理与应用(第3版)[M]. 武汉: 武汉大学出版社.
	SUN Jia-bing. 2013. Remote Sensing Principle and Application: 3rd Edition[M]. Wuhan University Press, Wuhan (in Chinese).
[7]	王晓青, 窦爱霞, 丁香, 等. 2015. 地震烈度应急遥感评估研究与应用进展[J]. 地球信息科学学报, 17(12): 1536-1544. DOI
	WANG Xiao-qing, DOU Ai-xia, DING Xiang, et al. 2015. Advance on the RS-based emergency seismic intensity assessment[J]. Journal of Geo-information Science, 17(12): 1536-1544 (in Chinese).
[8]	王晓青, 窦爱霞, 孙国清, 等. 2013. 基于综合震害指数的玉树地震烈度遥感评估研究[J]. 地震, 33(2): 1-10.
	WANG Xiao-qing, DOU Ai-xia, SUN Guo-qing, et al. 2013. Intensity assessment of the 2010 Yushu M_S7.1 earthquake based on synthetic seismic damage index[J]. Earthquake, 33(2): 1-10 (in Chinese).
[9]	肖本夫, 吴今生, 毛利, 等. 2021. 基于深度学习网络的地震地质灾害识别研究: 以四川九寨沟7.0级地震为例[J]. 震灾防御技术, 16(4): 617-624.
	XIAO Ben-fu, WU Jin-sheng, MAO Li, et al. 2021. Deep learning approach for seismic geohazard detection: A case study in Jiuzhaigou M_S7.0 earthquake, Sichuan, 2017[J]. Technology for Earthquake Disaster Prevention, 16(4): 617-624 (in Chinese).
[10]	袁小祥, 王晓青, 丁香, 等. 2017. 基于无人机影像的九寨沟地震建筑物震害定量评估[J]. 中国地震, 33(4): 582-589.
	YUAN Xiao-xiang, WANG Xiao-qing, DING Xiang, et al. 2017. Quantitative assessment of building damage in disaster area of the Jiuzhaigou earthquake based on UAV images[J]. Earthquake Research in China, 33(4): 532-589 (in Chinese).
[11]	袁小祥, 王晓青, 窦爱霞, 等. 2012. 基于地面LiDAR玉树地震地表破裂的三维建模分析[J]. 地震地质, 34(1): 39-46. doi: 10.3969/j.issn.0253-4967.2012.01.005. DOI
	YUAN Xiao-xiang, WANG Xiao-qing, DOU Ai-xia, et al. 2012. Terrestrial LiDAR-based 3D modeling analysis of surface rupture caused by Yushu earthquake[J]. Seismology and Geology, 34(1): 39-46 (in Chinese).
[12]	张景发, 李强, 张庆云, 等. 2018. 多源遥感图像的1976年 M_S7.8 唐山大地震等烈度区判定[J]. 遥感学报, 22(S1): 162-173.
	ZHANG Jing-fa, LI Qiang, ZHANG Qing-yun, et al. 2018. Intensity zone of the 1976 M_S7.8 Tangshan earthquake based on multisource remote sensing images[J]. Journal of Remote Sensing, 22(S1): 162-173 (in Chinese).
[13]	张永久, 彭立国, 程万正. 2006. 马尔康地震序列震源参数研究[J]. 中国地震, 22(1): 85-93.
	ZHANG Yong-jiu, PENG Li-guo, CHENG Wan-zheng. 2006. Study on source parameters of the Maerkang earthquake sequence[J]. Earthquake Research in China, 22(1): 85-93 (in Chinese).
[14]	中国地震局. 2018. 地震灾害遥感评估建筑物破坏(DB/T 75-2018)[S]. 北京: 中国标准出版社.
	China Earthquake Administration. 2018. Earthquake Disaster Assessment based on Remote Sensing: Building Damage(DB/ T 75-2018)[S]. Standards Press of China, Beijing (in Chinese).
[15]	中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. 2009. 建(构)筑物地震破坏等级划分(GB/T 24335-2009)[S]. 北京: 中国标准出版社.
	General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China,Standards Administration of China. 2009. Classification of Earthquake Damage to Buildings and Special Structures(GB/T 24335-2009)[S]. Standards Press of China, Beijing (in Chinese).
[16]	Axel C, van Aardt J. 2017. Building damage assessment using airborne lidar[J]. Journal of Applied Remote Sensing, 11(4): 046024. doi: 10.1117/1.jrs.11.046024. DOI
[17]	Cao C, Liu D, Singh R P, et al. 2016. Integrated detection and analysis of earthquake disaster information using airborne data[J]. Geomatics, Natural Hazards and Risk, 7(3): 1099-1128. DOI URL
[18]	Chaidas K, Tataris G, Soulakellis N. 2021. Seismic damage semantics on post-earthquake LOD3 building models generated by UAS[J]. International Journal of Geo-Information, 10(5): 345. doi: 10.3390/ijgi10050345. DOI
[19]	Cohen J A. 1960. A coefficient of agreement for nominal scales[J]. Educational and Psychological Measurement, 20(1): 37-46. DOI URL
[20]	Congalton R G. 1991. A review of assessing the accuracy of classifications of remotely sensed data[J]. Remote Sensing of Environment, 37(1): 270-279.
[21]	Dong L, Shan J. 2013. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 84: 85-99. DOI URL
[22]	Gerke M, Norman K. 2011. Automatic structural seismic damage assessment with airborne oblique pictometryimagery[J]. Photogrammetric Engineering and Remote Sensing, 77(9): 885-898. DOI URL
[23]	Ghaffarian S, Kerle N, Pasolli E, et al. 2019. Post-disaster building database updating using automated deep learning: An integration of pre-disaster OpenStreetMap and multi-temporal satellite data[J]. Remote Sensing, 11(20): 2427. doi: 10.3390/rs11202427. DOI URL
[24]	Gong L, An L, Liu M, et al. 2012. Road damage detection from high-resolution RS image[C]. 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, IEEE: 990-993.
[25]	Ji M, Liu L, Buchroithner M. 2018. Identifying collapsed buildings using post-earthquake satellite imagery and convolutional neural networks: A case study of the 2010 Haiti earthquake[J]. Remote Sensing, 10(11): 1689. doi: 10.3390/rs10111689. DOI URL
[26]	Kerle N, Hoffman R R. 2013. Collaborative damage mapping for emergency response: The role of cognitive systems engineering[J]. Natural Hazards and Earth System Sciences, 13(1): 97-113.
[27]	Kerle N, Nex F, Gerke M, et al. 2020. UAV-based structural damage mapping: A review[J]. International Journal of Geo-Information, 9(1): 14. doi: 10.3390/ijgi9010014. DOI
[28]	Li Y, Hu W, Dong H, et al. 2019. Building damage detection from post-event aerial imagery using single shot multibox detector[J]. Applied Sciences, 9(6): 1128. doi: 10.3390/app9061128. DOI URL
[29]	Ma S, Xu C. 2019. Assessment of co-seismic landslide hazard using the Newmark model and statistical analyses: A case study of the 2013 Lushan, China, M_W6.6 earthquake[J]. Natural Hazards, 96(1): 389-412. DOI
[30]	Menderes A, Erener A, Sarp G. 2015. Automatic detection of damaged buildings after earthquake h by using remote sensing and information technologies[J]. Procedia Earth and Planetary Science, 15: 257-262. DOI URL
[31]	Miura H, Midorikawa S, Kerle N. 2013. Detection of building damage areas of the 2006 Central Java, Indonesia, earthquake through digital analysis of optical satellite images[J]. Earthquake Spectra, 29(2): 453-473. DOI URL
[32]	Sharma, R C, Tateishi R, Hara K, et al. 2017. Earthquake damage visualization(EDV)technique for the rapid detection of earthquake-induced damages using SAR data[J]. Sensors, 17(2): 235. doi: 10.3390/s17020235. DOI URL
[33]	Redweik P, Teves-Costa P, Vilas-Boas I, et al. 2017. 3D city models as a visual support tool for the analysis of buildings seismic vulnerability: The case of Lisbon[J]. International Journal of Disaster Risk Science, 8(3): 308-325. DOI URL
[34]	Soulakellis N, Vasilakos C, Chatzistamatis S, et al. 2020. Post-earthquake recovery phase monitoring and mapping based on UAS data[J]. International Journal of Geo-Information, 9(7): 447. doi: 10.3390/ijgi9070447. DOI
[35]	Vetrivel A, Gerke M, Kerle N, et al. 2015. Identification of damage in buildings based on gaps in 3D point clouds from very high resolution oblique airborne images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 15: 61-78.
[36]	Xu C. 2015. Preparation of earthquake-triggered landslide inventory maps using remote sensing and GIS technologies: Principles and case studies[J]. Geoscience Frontiers, 6(6): 825-836. DOI URL
[37]	Xu Z, Wu Y, Lu X, et al. 2020. Photo-realistic visualization of seismic dynamic responses of urban building clusters based on oblique aerial photography[J]. Advanced Engineering Informatics, 43:101025. doi: /10.1016/j.aei.2019.101025. DOI URL
[38]	Zhang R, Li H, Duan K, et al. 2020. Automatic detection of earthquake-damaged buildings by integrating UAV oblique photography and infrared thermal imaging[J]. Remote Sensing, 12(16): 2621. doi: 10.3390/rs12162621. DOI URL

无人机参数指标	M300 RTK技术参数	精灵4 Pro V2.0技术参数
重量	6.3kg(含2块电池)	1.4kg
最大飞行海拔高度	5000m(2110桨叶,起飞重量≤7kg)	6000m
悬停精度	垂直精度: ±0.1m;水平精度: ±0.1m	垂直精度: ±0.1m;水平精度: ±0.3m
最大旋转角速度	俯仰轴: 300°/s;航向轴: 100°/s	俯仰轴: 250°/s;航向轴: 150°/s
最大上升速度	运动模式: 6m/s;定位模式: 5m/s	运动模式: 6m/s;定位模式: 5m/s
最大下降速度	运动模式: 5m/s;定位模式: 4m/s	运动模式: 4m/s;定位模式: 3m/s
最大可承受风速	15m/s(7级风)	10m/s(5级风)

无人机参数指标	M300 RTK技术参数	精灵4 Pro V2.0技术参数
重量	6.3kg(含2块电池)	1.4kg
最大飞行海拔高度	5000m(2110桨叶,起飞重量≤7kg)	6000m
悬停精度	垂直精度: ±0.1m;水平精度: ±0.1m	垂直精度: ±0.1m;水平精度: ±0.3m
最大旋转角速度	俯仰轴: 300°/s;航向轴: 100°/s	俯仰轴: 250°/s;航向轴: 150°/s
最大上升速度	运动模式: 6m/s;定位模式: 5m/s	运动模式: 6m/s;定位模式: 5m/s
最大下降速度	运动模式: 5m/s;定位模式: 4m/s	运动模式: 4m/s;定位模式: 3m/s
最大可承受风速	15m/s(7级风)	10m/s(5级风)

无人机荷载参数指标	MS Smart2技术参数	CMOS技术参数
镜头数量	5个	1个
传感器尺寸	22.3×14.9mm	12.8×9.6mm
照片尺寸	6000×4000Pix	5472×3078Pix
最小拍照间隔	0.2s	2s
有效像素	2430万(总像素>1.2亿)	2000万

无人机荷载参数指标	MS Smart2技术参数	CMOS技术参数
镜头数量	5个	1个
传感器尺寸	22.3×14.9mm	12.8×9.6mm
照片尺寸	6000×4000Pix	5472×3078Pix
最小拍照间隔	0.2s	2s
有效像素	2430万(总像素>1.2亿)	2000万

破坏等级	情景可视化解译指标	典型震害(正射视角)	典型震害(倾斜视角)
基本完好	建造规整,房屋轮廓清晰,建筑物与周边环境纹理或色调分界清晰,周边环境色调过渡自然,侧立面整齐,外墙和窗户无破坏。
轻微破坏	建造规整,房屋轮廓清晰,建筑物与周边环境纹理或色调分界清晰,屋顶覆盖的少数片石因轻微位移呈相对亮斑状,侧立面整齐,少数外墙出现一般性裂缝,极少数窗户有破坏。
中等破坏	建筑物与周边环境纹理或色调过度相对模糊,屋顶覆盖的多数片石因明显位移或轻微落瓦呈明显亮斑状。部分外墙有少数“X”型裂缝和墙皮脱落,纹理不规则,部分窗户出现变形和破坏。
严重破坏	屋顶覆盖的多数片石因明显位移或落瓦呈明显亮斑状,部分屋顶坍塌导致纹理模糊或点云差异。侧立面不规整,多数外墙有贯穿性裂缝和墙皮脱落现象,纹理极不规则,多数窗户出现变形和破坏。
毁坏	屋顶覆盖的片石因明显位移或落瓦呈明显亮斑状,屋顶部分坍塌或整体坍塌,导致纹理混乱或点云明显差异,侧立面残缺或极不规整。

基于倾斜摄影技术的典型情景可视化震害定量评估--以四川马尔康震群为例

QUANTITATIVE ASSESSMENT OF SEISMIC DAMAGE IN TYPICAL SCENARIOS BASED ON OBLIQUE AERIAL PHOTOGRAPHY: THE CASE OF THE MAERKANG EARTHQUAKE SWARM, SICHUAN, CHINA

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分类精度评价		现场调查
分类精度评价		毁坏	严重破坏	中等破坏	轻微破坏	基本完好	合计	UA/%
情景可视化解译	毁坏	6	0	0	0	0	6	100
	严重破坏	0	36	0	0	0	36	100
	中等破坏	0	16	276	0	0	292	95
	轻微破坏	0	1	11	145	2	159	91
	基本完好	0	0	1	10	16	27	59
	合计	6	53	288	155	18	520
	PA/%	100	68	96	94	89