Saving Cultural Heritage with Digital Make-Believe: Machine Learning and Digital Techniques to the Rescue

Deep learning proves effective for semantic annotation of cultural heritage image data, offering a promising solution for preserving at-risk sites. This study introduces a framework combining digital image processing, multi-lingual text analysis, and 3D modeling to enhance archiving, search, and analysis capabilities. The approach aims to create immersive cultural experiences, assess damage, reconstruct virtual sites, and provide educational augmented information—critical in regions like Palmyra, where physical access is limited or unsafe. Initial evaluations demonstrate the potential of computational methods to transform how heritage data is curated and disseminated, bridging gaps between physical preservation and digital innovation.

Yasser, A. M., Clawson, K., & Bowerman, C. (2017). Saving Cultural Heritage with Digital Make-Believe: Machine Learning and Digital Techniques to the Rescue. Electronic Workshops in Computing. https://doi.org/10.14236/ewic/hci2017.97