A comprehensive review of deep learning methods in damage classification, detection, and segmentation of cultural heritage sites
ElBehairy A, Abu El-Nasr NA, Grimberg P, Said LA (2026)
Publication Type: Journal article, Review article
Publication year: 2026
Journal
Book Volume: 78
Pages Range: 228-237
DOI: 10.1016/j.culher.2026.01.015
Abstract
Deep learning techniques are increasingly used to monitor and assess damage in cultural heritage sites. This paper reviews recent advances in deep learning for classifying, detecting, and segmenting damage in the context of heritage preservation. Classification methods identify the type of damage (e.g., cracks, mould) but lack detailed spatial information. Detection methods use bounding boxes to localize damaged regions, thereby simplifying damage monitoring. Segmentation methods provide pixel-level mapping of damage; hence, they are useful for documenting complex structures and surfaces. However, all segmentation-based approaches require large datasets and computational resources. This review systematically compares these three methodologies, discussing the strengths and limitations of each with respect to dataset requirements, spatial precision, and computational demands. In addition, the application of hybrid models, transfer learning, and the combination of deep learning with traditional image processing methods are discussed in the context of cultural preservation. Based on this discussion, suitable approaches are suggested for different heritage monitoring tasks and scenarios. Furthermore, the paper outlines potential directions for further research.
Authors with CRIS profile
Involved external institutions
Turkey (TR)
Egypt (EG)How to cite
APA:
ElBehairy, A., Abu El-Nasr, N.A., Grimberg, P., & Said, L.A. (2026). A comprehensive review of deep learning methods in damage classification, detection, and segmentation of cultural heritage sites. Journal of Cultural Heritage, 78, 228-237. https://doi.org/10.1016/j.culher.2026.01.015
MLA:
ElBehairy, Aya, et al. "A comprehensive review of deep learning methods in damage classification, detection, and segmentation of cultural heritage sites." Journal of Cultural Heritage 78 (2026): 228-237.
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