Generating Lost Urban Fabric: Exploration of Generative Adversarial Networks as a Design Tool in Post-Disaster Urban Recovery

Abstract

This study investigates the use of GANs, particularly the Pix2PixHD, for reconstructing urban fabric and preserving urban memory in post-disaster contexts, focusing on Hatay, Türkiye, after the 2023 earthquakes. Models were trained on pre-disaster urban maps and tested on incomplete post-earthquake data to regenerate damaged urban areas. Evaluation metrics, including FID scores, SSIM values, and visual inspections, demonstrated the model's ability to produce contextually accurate designs. The trained model effectively maintained road networks, building geometries, and spatial coherence. In addition to spatial consistency, the model produced outputs with sharp edges and high visual clarity. These results highlight the significant potential of GANs as generative design tools, offering valuable support to urban planners and architects in balancing urgent reconstruction needs with the long-term preservation of urban identity and memory in disaster-affected areas. © 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.