Rapid Fine-Grained Damage Assessment of Buildings on a Large Scale: A Case Study of the February 2023 Earthquake in Turkey
High-resolution stereo satellite images (HRSSIs) have the potential to provide the accurate height and volume information, playing a crucial role in assessing building collapses during various natural disasters. However, the time-consuming process of three-dimensional (3-D) reconstruction, inadequat...
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10423120/ |
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| author | Zhonghua Hong Hongyang Zhang Xiaohua Tong Shijie Liu Ruyan Zhou Haiyan Pan Yun Zhang Yanling Han Jing Wang Shuhu Yang |
| author_facet | Zhonghua Hong Hongyang Zhang Xiaohua Tong Shijie Liu Ruyan Zhou Haiyan Pan Yun Zhang Yanling Han Jing Wang Shuhu Yang |
| author_sort | Zhonghua Hong |
| collection | DOAJ |
| description | High-resolution stereo satellite images (HRSSIs) have the potential to provide the accurate height and volume information, playing a crucial role in assessing building collapses during various natural disasters. However, the time-consuming process of three-dimensional (3-D) reconstruction, inadequate vertical accuracy of digital surface model (DSM), and concentrated clustering of buildings pose challenges for collapse assessment focused on buildings. Therefore, we present an improved approach for rapid fine-grained assessment of building collapses. First, the accurate and consistent positioning parameters for HRSSIs are obtained through the combined block adjustment using laser altimetry points, ensuring the generation of DSMs with vertical accuracy exceeding 2 m. Next, a set of rapid 3-D reconstruction techniques is introduced, achieving a significant eightfold improvement in generating DSMs. Subsequently, we deploy an automated workflow for batch processing and registration of open-source building footprints, enabling the accurate extraction of building height changes from dual-time DSMs. Finally, based on the building change image, a large-scale GIS image of building floor-level collapses is generated using connected component detection and threshold classification strategies. These findings have far-reaching implications for postdisaster emergency response, damage assessment, and expeditious reconstruction efforts. In our study, we processed an 800 km<sup>2</sup> area in Kahramanmaras Province, Turkey, generating dual-time DSMs within 1 h. This enabled the assessment of floor-level collapses for a total of 48 092 buildings within the area. Validation was conducted on 361 houses in the city center, utilizing Google Street view images as ground truth. Remarkably, our approach achieved a high accuracy rate of 93.27% in floor-level assessment. |
| format | Article |
| id | doaj-art-8fa903127b2f45c3802420de92ffec7d |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-8fa903127b2f45c3802420de92ffec7d2025-08-20T02:55:53ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-01175204522010.1109/JSTARS.2024.336280910423120Rapid Fine-Grained Damage Assessment of Buildings on a Large Scale: A Case Study of the February 2023 Earthquake in TurkeyZhonghua Hong0https://orcid.org/0000-0003-0045-1066Hongyang Zhang1https://orcid.org/0009-0001-0568-9375Xiaohua Tong2https://orcid.org/0000-0002-1045-3797Shijie Liu3https://orcid.org/0000-0002-5941-0763Ruyan Zhou4https://orcid.org/0000-0003-4044-2340Haiyan Pan5https://orcid.org/0009-0004-5565-3022Yun Zhang6https://orcid.org/0000-0003-4367-8674Yanling Han7https://orcid.org/0000-0002-0682-9157Jing Wang8https://orcid.org/0000-0001-6063-9808Shuhu Yang9https://orcid.org/0000-0001-9967-7756College of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaHigh-resolution stereo satellite images (HRSSIs) have the potential to provide the accurate height and volume information, playing a crucial role in assessing building collapses during various natural disasters. However, the time-consuming process of three-dimensional (3-D) reconstruction, inadequate vertical accuracy of digital surface model (DSM), and concentrated clustering of buildings pose challenges for collapse assessment focused on buildings. Therefore, we present an improved approach for rapid fine-grained assessment of building collapses. First, the accurate and consistent positioning parameters for HRSSIs are obtained through the combined block adjustment using laser altimetry points, ensuring the generation of DSMs with vertical accuracy exceeding 2 m. Next, a set of rapid 3-D reconstruction techniques is introduced, achieving a significant eightfold improvement in generating DSMs. Subsequently, we deploy an automated workflow for batch processing and registration of open-source building footprints, enabling the accurate extraction of building height changes from dual-time DSMs. Finally, based on the building change image, a large-scale GIS image of building floor-level collapses is generated using connected component detection and threshold classification strategies. These findings have far-reaching implications for postdisaster emergency response, damage assessment, and expeditious reconstruction efforts. In our study, we processed an 800 km<sup>2</sup> area in Kahramanmaras Province, Turkey, generating dual-time DSMs within 1 h. This enabled the assessment of floor-level collapses for a total of 48 092 buildings within the area. Validation was conducted on 361 houses in the city center, utilizing Google Street view images as ground truth. Remarkably, our approach achieved a high accuracy rate of 93.27% in floor-level assessment.https://ieeexplore.ieee.org/document/10423120/Building damage assessmentchange detectionGaofen-7 (GF-7) high-resolution satellite stereo imagesthree-dimensional (3-D) reconstruction |
| spellingShingle | Zhonghua Hong Hongyang Zhang Xiaohua Tong Shijie Liu Ruyan Zhou Haiyan Pan Yun Zhang Yanling Han Jing Wang Shuhu Yang Rapid Fine-Grained Damage Assessment of Buildings on a Large Scale: A Case Study of the February 2023 Earthquake in Turkey IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Building damage assessment change detection Gaofen-7 (GF-7) high-resolution satellite stereo images three-dimensional (3-D) reconstruction |
| title | Rapid Fine-Grained Damage Assessment of Buildings on a Large Scale: A Case Study of the February 2023 Earthquake in Turkey |
| title_full | Rapid Fine-Grained Damage Assessment of Buildings on a Large Scale: A Case Study of the February 2023 Earthquake in Turkey |
| title_fullStr | Rapid Fine-Grained Damage Assessment of Buildings on a Large Scale: A Case Study of the February 2023 Earthquake in Turkey |
| title_full_unstemmed | Rapid Fine-Grained Damage Assessment of Buildings on a Large Scale: A Case Study of the February 2023 Earthquake in Turkey |
| title_short | Rapid Fine-Grained Damage Assessment of Buildings on a Large Scale: A Case Study of the February 2023 Earthquake in Turkey |
| title_sort | rapid fine grained damage assessment of buildings on a large scale a case study of the february 2023 earthquake in turkey |
| topic | Building damage assessment change detection Gaofen-7 (GF-7) high-resolution satellite stereo images three-dimensional (3-D) reconstruction |
| url | https://ieeexplore.ieee.org/document/10423120/ |
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