Earthquakes, as sudden-onset natural disasters with high destructive potential, not only result in significant casualties but also cause severe damage to infrastructure—particularly buildings—posing major challenges to post-disaster rescue and reconstruction efforts. In emergency response scenarios, the rapid and accurate assessment of building damage is a critical prerequisite for formulating effective rescue strategies and allocating resources efficiently. Traditional manual on-site investigation methods, however, present notable limitations. Disaster-affected areas often experience traffic disruptions and harsh environmental conditions, hindering timely access for investigators. Moreover, manual assessments are time-consuming and generally incapable of meeting the urgent demands of rescue operations within the critical 72-hour post-disaster window. Large-scale manual surveys also involve safety risks, potentially leading to secondary casualties. Therefore, the development of rapid, efficient, and accurate building damage assessment methods holds significant practical and strategic importance.
In response to this need, the this study we proposes an innovative rapid assessment method for earthquake-induced building damage using unmanned aerial vehicle(UAV)imagery combined with machine learning algorithms. This method leverages the advantages of UAV remote sensing—such as high mobility, flexibility, and high spatial resolution—together with advanced image processing and machine learning techniques to enable intelligent identification and assessment of building damage. The study focuses on the MS6.8 earthquake that struck Dingri County, Xizang, using it as a case study to validate the proposed methodology through a structured technical workflow. The assessment framework comprises three key stages. First, an object-oriented remote sensing classification(OORSC) approach was used to extract individual building features from UAV imagery. By employing rule-based classification strategies, this method effectively eliminates background noise such as trees and roads. After morphological filtering, the completeness of building boundary extraction exceeded 95%, and hole-filling performance was markedly improved, ensuring high-quality input data for subsequent analyses. Second, the study focused on the extraction and optimization of surface texture features. Using algorithms such as the Gray-Level Co-occurrence Matrix(GLCM) and Local Binary Pattern(LBP), critical parameters—including contrast, entropy, and variance—were derived. Experimental data show that, on average, the contrast of collapsed buildings is 26%lower than that of intact buildings, while entropy and variance increase by 32% and 41%, respectively. These features provide robust quantitative indicators for identifying structural damage. Lastly, the study implemented a comparative experimental design incorporating four technical routes, systematically evaluating the performance of classification algorithms such as Support Vector Machines(SVM) and Neural Networks(NN).
The results demonstrate that the neural network model integrating optimized texture features yields the best performance, achieving an overall accuracy(OA)of 91% and a Kappa coefficient of 0.8. Compared to models excluding texture features, the improvement is significant: the neural network model without texture features achieved an OA of 85% and a Kappa of 0.6, while the SVM-based approach achieved an OA of 82% and a Kappa of 0.6. The recognition accuracy by damage level further reveals that severely damaged buildings are most accurately identified(94%)due to their distinctive visual characteristics, followed by collapsed(87%)and moderately damaged buildings(80%). Misclassification of collapsed structures mainly stems from blurred textures in the imagery. These findings underscore the critical role of texture features in building damage identification and validate the proposed method’s effectiveness in supporting post-disaster emergency response.
Despite the promising results, the proposed method has several limitations in practical application. At the technical level, complex environmental backgrounds—such as similar roof materials and shadow effects—can interfere with detection accuracy and demand high image quality. At the data level, a lack of sufficient real-time ground truth data may compromise model training accuracy. At the application level, the method’s capacity to detect complex damage types—such as internal structural failures—remains limited. To address these challenges, future research will focus on several directions. From a technical innovation perspective, advanced methods such as deep learning will be explored, particularly the use of three-dimensional convolutional neural networks(3D-CNNs)for capturing volumetric building features. In terms of data integration, the fusion of multi-source data—such as LiDAR point clouds, digital surface models(DSM), and thermal infrared imagery—will be pursued to build a multimodal feature fusion framework. Methodologically, transfer learning and data augmentation will be applied to enhance model generalizability, and adaptive algorithms will be developed to manage complex and dynamic disaster scenarios. On the application front, the establishment of a standardized sample library and evaluation system is proposed to support the broader deployment and engineering application of the method.
The significance of this study is multidimensional. Theoretically, it introduces a novel approach to building damage identification based on texture features and machine learning, enriching the theoretical framework of remote sensing-based disaster assessment. Technologically, it develops a comprehensive UAV image processing and analysis pipeline, offering a replicable technical route for related research. Practically, the established system can be directly applied to post-earthquake emergency response, enhancing the efficiency and effectiveness of rescue operations. With continued technological advancement, the method holds potential for adaptation to other disaster scenarios, such as typhoons and floods, thereby contributing to integrated disaster risk reduction. Future work will continue to advance research in this area, targeting breakthroughs in key challenges such as multi-source data fusion and intelligent algorithm optimization, with the goal of advancing disaster assessment technologies toward greater intelligence and precision, ultimately contributing to the protection of lives and property and the promotion of sustainable development.
