Rapid Fine-Grained Damage Assessment of Buildings on a Large Scale: A Case Study of the February 2023 Earthquake in Turkey - Zhonghua Hong; Hongyang Zhang; Xiaohua Tong; Shijie Liu; Ruyan Zhou; Haiyan Pan; Yun Zhang; Yanling Han; Jing Wang; Shuhu Yang (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
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 km2 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.