Predicting Post-Disaster Damage Levels and Generating Post-Disaster Imagery from Pre-Disaster Satellite Images Using Pix2Pix

Abstract. Accurately forecasting disaster impacts before they occur is crucial for effective emergency preparedness and response. This study presents a dual approach utilizing the Pix2Pix conditional Generative Adversarial Network (cGAN) to leverage pre-disaster satellite imagery for enhanced disaster risk management. Firstly, we employ Pix2Pix to predict post-disaster damage levels from pre-disaster satellite images. By training on the xBD dataset, the model learns to generate spatially distributed damage predictions, enabling proactive planning and resource allocation in high-risk areas. Secondly, Pix2Pix is used to generate synthetic post-disaster images from pre-disaster inputs, allowing for scenario visualization without reliance on actual post-disaster imagery. The model's performance is evaluated using accuracy, precision, recall, and F1-score for damage prediction, achieving an accuracy of 79% and an F1-score of 76%. For synthetic image generation, structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) are used, yielding average values of 0.57 and 23.5, respectively. These results indicate the potential of our framework in anticipating disaster damage and generating realistic post-disaster visualizations. The framework's performance depends on the quality and availability of pre-disaster satellite imagery, which may affect prediction reliability. Further evaluation across different disaster types, including earthquakes, and wildfires, is needed to assess robustness and generalizability. This study demonstrates the potential of generative AI-based approaches in enhancing disaster preparedness by providing both damage forecasting and post-disaster image generation. The proposed framework supports decision-makers in emergency response, urban resilience planning, and risk mitigation strategies, contributing to more effective disaster management.