Remote Sensing Extraction of Damaged Buildings in the Shigatse Earthquake, 2025: A Hybrid YOLO-E and SAM2 Approach

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 14; p. 4375
• Main Authors: Wu, Zhimin, Qu, Chenyao, Wang, Wei, Miao, Zelang, Feng, Huihui
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.07.2025; MDPI
• Subjects: Accuracy; Adaptation; Architecture; Buildings; China; Classification; damaged building extraction; Datasets; Disaster relief; earthquake damage assessment; Earthquakes; Forecasts and trends; multi-source remote sensing; Natural disaster damage; Neural networks; Post-disaster reconstruction; Remote sensing; Satellites; Segment Anything Model 2 (SAM2); Semantics; Topography; Turkey; YOLO-E; China; Turkey; earthquake damage assessment; multi-source remote sensing; Segment Anything Model 2 (SAM2); YOLO-E; damaged building extraction
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25144375

In January 2025, a magnitude 6.8 earthquake struck Dingri County, Shigatse, Tibet, causing severe damage. Rapid and precise extraction of damaged buildings is essential for emergency relief and rebuilding efforts. This study proposes an approach integrating YOLO-E (Real-Time Seeing Anything) and the Segment Anything Model 2 (SAM2) to extract damaged buildings with multi-source remote sensing images, including post-earthquake Gaofen-7 imagery (0.80 m), Beijing-3 imagery (0.30 m), and pre-earthquake Google satellite imagery (0.15 m), over the affected region. In this hybrid approach, YOLO-E functions as the preliminary segmentation module for initial segmentation. It leverages its real-time detection and segmentation capability to locate potential damaged building regions and generate coarse segmentation masks rapidly. Subsequently, SAM2 follows as a refinement step, incorporating shapefile information from pre-disaster sources to apply precise, pixel-level segmentation. The dataset used for training contained labeled examples of damaged buildings, and the model optimization was carried out using stochastic gradient descent (SGD), with cross-entropy and mean squared error as the selected loss functions. Upon evaluation, the model reached a precision of 0.840, a recall of 0.855, an F1-score of 0.847, and an IoU of 0.735. It successfully extracted 492 suspected damaged building patches within a radius of 20 km from the earthquake epicenter, clearly showing the distribution characteristics of damaged buildings concentrated in the earthquake fault zone. In summary, this hybrid YOLO-E and SAM2 approach, leveraging multi-source remote sensing imagery, delivers precise and rapid extraction of damaged buildings with a precision of 0.840, recall of 0.855, and IoU of 0.735, effectively supporting targeted earthquake rescue and post-disaster reconstruction efforts in the Dingri County fault zone.