Vision Transformer based Damage Assessment from Post-Disaster Satellite Imagery: An Applied Study on Hurricane Harvey

The timely and accurate evaluation of building damage is vital for post-disaster response. This study evaluates the efficacy of Vision Transformers (ViT-B32) compared to a baseline Convolutional Neural Network (EfficientNet-B0) for the binary classification of damaged versus undamaged buildings, utilizing 128×128 RGB satellite imagery from Hurricane Harvey. As opposed to CNNs that encode local features, ViT structures rely on self-attention mechanisms for capturing global spatial relationships, a property that is crucial in spotting intricate damage patterns in visually noisy disaster sites. Experiments show that the ViT model outperforms the CNN baseline in terms of classification accuracy (97.85% vs 96.90%) and prove to be more robust on unbalanced data as shown with F1-score and AUC than respective values of close competitors. Furthermore, the study highlights the model's interpretability: we generate attention heatmaps that visualize the specific image regions driving the classification decisions. These visualizations provide actionable insights by precisely localizing structural damage, thereby offering a valuable tool for prioritizing recovery efforts in disaster management workflows.