Akademik Veri Yönetim Sistemi
Journal of Asian Earth Sciences, cilt.300, 2026 (SCI-Expanded, Scopus)
Earthquakes cause severe damage to buildings and infrastructure, posing risks to humans and disrupting urban systems. Traditional ground-based assessments are slow and often impractical in dense urban areas. This study evaluates several machine learning algorithms for detecting damaged buildings after the 6 February 2023 earthquakes in southeastern Turkey, focusing on the city center of Hatay, one of the most affected regions. High-resolution post-event Pléiades imagery and building footprints were used to create training and test datasets covering more than 8,000 structures, and textural features extracted from the imagery served as predictors. We tested artificial neural network (ANN), random forest (RF), support vector machine (SVM) with different kernels, ensemble learners, and multivariate adaptive regression splines (MARS). To our knowledge, this is the first application of MARS to post-earthquake damage mapping using very high-resolution optical data. Model performance was assessed using 10-fold cross-validation, overall accuracy (OA), and F1 score. RF reached high training accuracy (∼0.95) but showed limited generalization. SVM with an RBF kernel behaved similarly, while polynomial SVM (degree 2) performed the worst. MARS produced moderate but stable results across folds. ANN and linear SVM showed comparable performance, with slightly higher stability for the latter. The ensemble model yielded the best test results (OA = 0.59, F1 = 0.66), offering a balance of accuracy, robustness, and computational efficiency. Although accuracies remained modest, the workflow can generate a usable damage map within 1–2 h after acquiring the post-event Pléiades image, making it suitable for rapid first-pass screening in operational settings.