Rapid post-earthquake damage assessment using patch-level CNNs and VLM

Abstract Rapid and accurate post-earthquake damage assessment is critical for effective disaster response and enabling sustainable community recovery. This study addresses the urgent need for timely and precise quantification of earthquake-induced building damage, moving beyond simple detection, to inform actionable engineering decisions. It explores AI-driven image classification to expedite damage evaluation by identifying damaged buildings from post-disaster photos much faster than conventional methods. A total of 154,380 indoor image patches (224 × 224 pixels) from photos of buildings affected by the 2020 M6.4 Petrinja, Croatia earthquake, were compiled. These patches include both close-up cracks and wider building context. Two convolutional neural networks (ResNet-18 and ConvNeXt-Tiny) were trained on the patch dataset for binary damage classification. Additionally, large vision-language model (VLM) Gemma 3 27B was evaluated in a zero-shot binary classification setting by providing it with a descriptive prompt to classify each patch as damaged or undamaged. ConvNeXt-Tiny achieved the highest performance, with a macro F1-score of 0.849 and a damaged-class F1-score of 0.731, slightly outperforming ResNet-18. Gemma 3 27B achieved a macro F1-score of approximately 0.6 but a damaged-class F1-score of 0.366, with many false positives. Integrating such a granular approach into rapid loss assessment frameworks significantly enhances decision-making capabilities and post-disaster recovery efforts, providing a more detailed understanding of structural integrity across affected areas.