Reconstruction of Cultural Heritage in Virtual Space Following Disasters

While previous studies have explored the use of digital technologies in cultural heritage site reconstruction, limited attention has been given to systems that simultaneously support cultural restoration and psychological healing. This study investigates how multimodal, deep learning–assisted digital technologies can aid displaced populations by enabling both digital reconstruction and trauma relief within virtual environments. A demonstrative virtual reconstruction workflow was developed using the Great Mosque of Aleppo in Damascus as a case study. High-precision three-dimensional models were generated using Neural Radiance Fields, while Stable Diffusion was applied for texture style transfer and localized structural refinement. To enhance immersion, Vector Quantized Variational Autoencoder–based audio reconstruction was used to embed personalized ambient soundscapes into the virtual space. To evaluate the system’s effectiveness, interviews, tests, and surveys were conducted with 20 refugees aged 18–50 years, using the Impact of Event Scale-Revised and the System Usability Scale as assessment tools. The results showed that the proposed approach improved the quality of digital heritage reconstruction and contributed to psychological well-being, offering a novel framework for integrating cultural memory and emotional support in post-disaster contexts. This research provides theoretical and practical insights for future efforts in combining cultural preservation and psychosocial recovery.