| Citation: |
LI Jiayi, HUANG Xin, HU Yuping, ZHANG Zhen, ZHANG Xueting, ZHANG Shulei, FANG Xing. Fusion of Optical Daily and Night-time Light Remote Sensing Images for Collapsed Building Detection: A Case in Turkey Mw 7.8 Earthquake[J]. Geomatics and Information Science of Wuhan University, 2023, 48(10): 1706-1714. DOI: 10.13203/j.whugis20230275
|
The Mw 7.8 earthquake occurred in Turkey on February 6th, 2023, which is a rare powerful earthquake in recent years. Building collapse detection is an important part of post-earthquake damage assessment and emergency rescue. Facing the emergency rescue and post-earthquake disaster assessment, this paper aims to comprehensively utilize the intelligent interpretation technology of satellite remote sensing, coupled with high-resolution optical daily and night-time light remote sensing images, and to efficiently assess the collapsed buildings in the extreme disaster areas with low time and labor costs.
In this paper, we present a multi-level rapid localization scheme for disaster-affected regions. First, the proposed approach involves the utilization of low- and medium-resolution night-time light data to estimate the extent of extreme disaster areas. Subsequently, high-resolution optical remote sensing imagery is employed to extract the collapsed buildings. By training a semi-supervised deep learning network with a small number of manually collected samples, the scheme automatically identifies collapse areas, thereby facilitating the precise localization of collapsed buildings. A high-resolution architecture network, HRNet, is employed in this study. HRNet has the advantages in both sematic and spatial feature representation, and can capture the collapsed buildings with muti-scale characteristics.
Six cities along the border between the Arab Plate and the Eurasian Plate are severely affected, from Adelman to Kahraman Marrash, and then to Antakia. A total of 2 377 collapsed buildings are detected in the extreme disaster areas. Except for Nurdaji and Antakia cities, where the proportion of collapsed buildings exceeded 2%, the proportion of collapsed buildings in the other four cities is close to 1%. After manual verification, the recall rate of the intelligent interpretation plan is between 74% and 93%.
The efficiency and effectiveness of this approach are demonstrated through its application in assessing the building collapse situation after the Mw 7.8 earthquake in Turkey. The proposed work can serve as a timely reference for macro decision-making in post-earthquake emergency rescue operations.
| [1] |
田丽莉. 地震灾害人员伤亡影响因素分析及人员伤亡估算公式[D]. 北京: 首都经济贸易大学, 2012.
Tian Lili. Analysis of Factors Affecting Casualties in Earthquake Disasters and Estimation Formulas for Casualties[D]. Beijing: Capital University of Economics and Trade, 2012.
|
| [2] |
眭海刚, 刘超贤, 黄立洪, 等. 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), 2019, 44(7): 1008-1019. doi: 10.13203/j.whugis20190070
Sui Haigang, Liu Chaoxian, Huang Lihong, et al. Application of Remote Sensing Technology in Earthquake-Induced Building Damage Detection[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 1008-1019. doi: 10.13203/j.whugis20190070
|
| [3] |
陈鑫连. 地震灾害的航空遥感信息快速评估与救灾决策[M]. 北京: 科学出版社, 1995.
Chen Xinlian. Quick Evaluation and Decision-making for Earthquake Disaster by Airborne Remote Sensing Information[M]. Beijing: Science Press, 1995.
|
| [4] |
和海霞, 武斌, 李儒, 等. 面向自然灾害应急的卫星协同观测策略研究[J]. 航天返回与遥感, 2018, 39(6): 91-101. https://www.cnki.com.cn/Article/CJFDTOTAL-HFYG201806014.htm
He Haixia, Wu Bin, Li Ru, et al. Research on Requirements and Strategies of Satellite Cooperative Observation for Natural Disaster Emergency Response[J]. Spacecraft Recovery & Remote Sensing, 2018, 39(6): 91-101. https://www.cnki.com.cn/Article/CJFDTOTAL-HFYG201806014.htm
|
| [5] |
李素菊, 刘明. 卫星遥感在我国重大地震灾害应急监测中的应用[J]. 城市与减灾, 2018(4): 7-10. https://www.cnki.com.cn/Article/CJFDTOTAL-CSJZ201804003.htm
Li Suju, Liu Ming. Application of Satellite Remote Sensing in Emergency Monitoring of Major Earthquake Disaster in China[J]. City and Disaster Reduction, 2018(4): 7-10. https://www.cnki.com.cn/Article/CJFDTOTAL-CSJZ201804003.htm
|
| [6] |
叶昕, 秦其明, 王俊, 等. 利用高分辨率光学遥感图像检测震害损毁建筑物[J]. 武汉大学学报(信息科学版), 2019, 44(1): 125-131. doi: 10.13203/j.whugis20150490
Ye Xin, Qin Qiming, Wang Jun, et al. Detecting Damaged Buildings Caused by Earthquake from Remote Sensing Image Using Local Spatial Statistics Method[J]. Geomatics and Information Science of Wuhan University, 2019, 44(1): 125-131. doi: 10.13203/j.whugis20150490
|
| [7] |
史梦圆. 基于深度学习的建筑物震害检测方法研究[D]. 西安: 西安工业大学, 2023.
Shi Mengyuan. Research on Building Seismic Damage Detection Method Based on Deep Learning[D]. Xi'an: Xi'an Technological University, 2023.
|
| [8] |
马豪杰. 基于深度学习的震后遥感影像建筑物损毁信息提取研究[D]. 北京: 中国科学院大学, 2020.
Ma Haojie, Research on Extracting Building Damage Information from Postearthquake Remote Sensing Images Based on Deep Learning[D]. Beijing: University of Chinese Academy of Sciences, 2020.
|
| [9] |
吴禄源, 仝敬博, 王自法, 等. 基于深度卷积神经网络和迁移学习的农村房屋洪涝灾害后受损等级分类[J]. 地球科学, 2023, 48(5): 1742-1754. https://www.cnki.com.cn/Article/CJFDTOTAL-DQKX202305006.htm
Wu Luyuan, Tong Jingbo, Wang Zifa, et al. Classification of Damaged Grade on Rural Houses After Flood Disaster Based on Deep Convolutional Neural Network and Transfer Learning[J]. Earth Science, 2023, 48(5): 1742-1754. https://www.cnki.com.cn/Article/CJFDTOTAL-DQKX202305006.htm
|
| [10] |
眭海刚, 黄立洪, 刘超贤. 利用具有注意力的Mask R-CNN检测震害建筑物立面损毁[J]. 武汉大学学报(信息科学版), 2020, 45(11): 1660-1668. doi: 10.13203/j.whugis20200158
Sui Haigang, Huang Lihong, Liu Chaoxian. Detecting Building Facade Damage Caused by Earthquake Using CBAM-improved Mask R-CNN[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1660-1668. doi: 10.13203/j.whugis20200158
|
| [11] |
Mouloud H, Chaker A, Nassim H, et al. Post-earthquake Damage Classification and Assessment: Case Study of the Residential Buildings After the Mw = 5 Earthquake in Mila City, Northeast Algeria on August 7, 2020[J]. Bulletin of Earthquake Engineering, 2023, 21(2): 849-891.
|
| [12] |
Robinson C, Gupta R, Nsutezo S F, et al. Turkey Earthquake Report [R]. New York: Microsoft, 2023.
|
| [13] |
黄昕, 李家艺, 杨杰, 等. Landsat卫星观测下的30m全球不透水面年度动态与城市扩张模式(1972—2019)[J]. 中国科学: 地球科学, 2021, 51(11): 1894-1906. https://www.cnki.com.cn/Article/CJFDTOTAL-JDXK202111005.htm
Huang Xin, Li Jiayi, Yang Jie, et al. 30 m Global Impervious Surface Area Dynamics and Urban Expansion Pattern Observed by Landsat Satellites: From 1972 to 2019[J]. Scientia Sinica (Terrae), 2021, 51(11): 1894-1906. https://www.cnki.com.cn/Article/CJFDTOTAL-JDXK202111005.htm
|
| [14] |
Román M O, Wang Z S, Sun Q S, et al. NASA's Black Marble Nighttime Lights Product Suite[J]. Remote Sensing of Environment, 2018, 210: 113-143.
|
| [15] |
Sun K, Xiao B, Liu D, et al. Deep High-Resolution Representation Learning for Human Pose Estimation[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019.
|
| [1] | HUANG Motao, OUYANG Yongzhong, BIAN Shaofeng, LI Shanshan, LI Mingsan, LU Xiuping, WANG Weiping, DONG Chao, TANG Minqiang, HONG Lidan, HOU Guangchao. Analysis and Reflections on the Development of Underwater Gravity-Aided Inertial Navigation Technology in the United States and Russia[J]. Geomatics and Information Science of Wuhan University, 2024, 49(11): 1977-1991. DOI: 10.13203/j.whugis20240228 |
| [2] | LI Qingquan, CHEN Ruizhe, TU Wei, CHEN Zhipeng, ZHANG Bochen, YAN Aiguo, YIN Pengcheng. Real-Time Vision-Based Deformation Measurement of Long-Span Bridge with Inertial Sensors[J]. Geomatics and Information Science of Wuhan University, 2023, 48(11): 1834-1843. DOI: 10.13203/j.whugis20230006 |
| [3] | WU Yanxiong, TENG Yuntian, WU Qiong, XU Xing, ZHANG Bing. Error Correction Model and Uncertainty Analysis of the Shipborne Absolute Gravity Measurement System[J]. Geomatics and Information Science of Wuhan University, 2022, 47(4): 492-500. DOI: 10.13203/j.whugis20190412 |
| [4] | ZHA Feng, HE Hongyang, LI Zhiwei, LI Jingshu. A SINS Initial Alignment Method Using Improved Parameter Identification[J]. Geomatics and Information Science of Wuhan University, 2020, 45(7): 974-979. DOI: 10.13203/j.whugis20180312 |
| [5] | GUAN Bin, SUN Zhongmiao, WU Fumei, LIU Xiaogang. Influence of Horizontal Disturbing Gravity on Position Error in Inertial Navigation Systems[J]. Geomatics and Information Science of Wuhan University, 2017, 42(10): 1474-1481. DOI: 10.13203/j.whugis20160006 |
| [6] | QIN Fangjun, LI An, XU Jiangning. Analysis of Errors of Rotating Modulation INS Effected by Angular Motion of Vehicle[J]. Geomatics and Information Science of Wuhan University, 2012, 37(7): 831-833. |
| [7] | QIN Fangjun, XU Jiangning, LI An. A New Calculative Method for Gyro-Free Inertial Navigation System Using 9Accelerometers[J]. Geomatics and Information Science of Wuhan University, 2012, 37(3): 278-281. |
| [8] | OUYANG Yongzhong, LU Xiuping, HUANG Motao, ZHAI Guojun. An Integrated Method for Compensating the Systematic Errors of Marine and Airborne Measurements from L&R Gravimeter[J]. Geomatics and Information Science of Wuhan University, 2011, 36(5): 625-629. |
| [9] | JIN Jihang, BIAN Shaofeng. Analysis of Inertial Navigation System Positioning Error Caused by Gravity Disturbance[J]. Geomatics and Information Science of Wuhan University, 2010, 35(1): 30-32. |
| [10] | YANG Tao, WANG Wei, ZHU Zhiqin. Analysis and Verification of Time Synchronization Error in GPS/SINS Integrated System[J]. Geomatics and Information Science of Wuhan University, 2009, 34(10): 1181-1184. |
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: