Comparative analysis of deep feature fusion and machine learning classifiers for UAV imagery in post-earthquake building damage assessment
Abstract
The rapid and accurate assessment of building damage after earthquakes is critically essential for search-and-rescue and humanitarian-aid operations. This study proposes a comprehensive hybrid intelligent system to classify buildings into three categories—intact, damaged, and destroyed—using the UAV-TEBDE dataset, which comprises high-resolution Unmanned Aerial Vehicle (UAV) images collected after earthquakes in Türkiye. The proposed methodology is based on the fusion of deep features extracted from five different pretrained Convolutional Neural Network (CNN) models, including ResNet50, EfficientNetB4, VGG16, DenseNet121, and MobileNetV2, using a transfer learning approach. These enriched, high-dimensional combined feature vectors were systematically used to compare the performance of 12 machine learning classifiers, including ensemble learning methods, support vector machines, and discriminant analyses. The experimental results, validated through a robust 10-fold Stratified Group Cross-Validation, demonstrated that the proposed feature-level (early) fusion strategy achieved outstanding success. The Quadratic Discriminant Analysis (QDA) model exhibited the highest performance, attaining a mean Weighted F1 Score of 99.53% (±0.09%), surpassing more complex ensemble models and neural networks. The exceptionally low standard deviation observed across the validation folds confirmed that the superior performance of the QDA model was statistically robust and consistent. This study revealed that CNN-based feature fusion yields a highly distinctive feature space for post-disaster damage assessment, thereby enabling rapid near-perfect automatic damage mapping.
Keywords
Deep learning , Earthquake damage assessment , Image fusion , Machine learning
Kaynakça
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