Keywords:-
Article Content:-
Abstract
Non-destructive evaluation (NDE) of heritage structures is essential and necessary for effective conservation with long-term preservation. In recent years, deep learning techniques with the help of computer vision have emerged as powerful tools for analyzing and detecting damage in historic monuments and its structural assessment. This methodological review examines progression from classical machine learning approaches to convolutional neural networks, generative adversarial networks, and attention-based models is analyzed. This paper also highlight their roles in damage detection and monument recognition. It compares classification and segmentation techniques based on their methodological strengths, limitations and suitability for various heritage sites.
The discussion covers key challenges such as data scarcity and practical application of these methods in real world scenario. Future research directions are outlined to help develop reliable and interpret able AI-based tools for heritage conservation.
References:-
References
Bhatt, M. S., & Patalia, T. P. (2017). Indian monuments classification using support vector machine. International Journal of Electrical and Computer Engineering, 7(4), 1952. 10.11591/ijece.v7i4.pp1952-1963
Mesanza-Moraza, A., García-Gómez, I., & Azkarate, A. (2020). Machine learning for the built heritage archaeological study. Journal on Computing and Cultural Heritage (JOCCH), 14(1), 1-21 https://doi.org/10.1145/3422993
Wang, N., Zhao, Q., Li, S., Zhao, X., & Zhao, P. (2018). Damage classification for masonry historic structures using convolutional neural networks based on still images. Computer‐Aided Civil and Infrastructure Engineering, 33(12), 1073-1089. https://doi.org/10.1111/mice.12411
Kim, B., & Cho, S. (2019). Image‐based concrete crack assessment using mask and region‐based convolutional neural network. Structural Control and Health Monitoring, 26(8), e2381 https://doi.org/10.1002/stc.2381
Liu, Z., Cao, Y., Wang, Y., & Wang, W. (2019). Computer vision-based concrete crack detection using U-net fully convolutional networks. Automation in Construction, 104, 129-139. https://doi.org/10.1016/j.autcon.2019.04.005
Bayram, B., Kılıç, B., Özoğlu, F., Erdem, F., Bakirman, T., Sivri, S., & Delen, A. (2020). A deep learning integrated mobile application for historic landmark recognition: A case study of Istanbul. Mersin Photogrammetry Journal, 2(2), 38-50.
Hatir, M. E., Barstuğan, M., & İnce, İ. (2020). Deep learning-based weathering type recognition in historical stone monuments. Journal of Cultural Heritage, 45, 193-203. https://doi.org/10.1016/j.culher.2020.04.008
He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969). 10.1109/ICCV.2017.322
Wang, N., Zhao, X., Zou, Z., Zhao, P., & Qi, F. (2020). Autonomous damage segmentation and measurement of glazed tiles in historic buildings via deep learning. Computers Aided Civil and Infrastructure Engineering, 35(3), 277–291.
https://doi.org/10.1111/mice.12488
Liu, Y., Wang, Y., & Liu, C. (2023). A deep learning method using auto encoder and generative adversarial network for anomaly detection on ancient stone stele surfaces. arXiv. DOI:10.48550/arXiv.2308.04426
K. Kishan, G. Navya, B. Abhilash Reddy, A. Pranith (2025). Advance legacy Monuments Categorizing using deep learning. International Research Journal of Modernization in Engineering Technology and Science, 7(1), 4728-4734.
Jyoti Paul, A., Ghose, S., Aggarwal, K., Nethaji, N., Pal, S., & Dutta Purkayastha, A. (2021). Machine Learning Advances aiding Recognition and Classification of Indian Monuments and Landmarks. arXiv e-prints, arXiv-2107.
https://doi.org/10.48550/arXiv.2107.14070
Shelke, S. M., Shahale, K. A., Pathak, I. S., Lunge, D. V., Sangai, A. P., & Vyawahare, H. R. (2023). Indian heritage monuments identification using deep learning methodologies. SSGM Journal of Science and Engineering, 1(1), 52-56.
Siountri, K., & Anagnostopoulos, C. N. (2023). The classification of cultural heritage buildings in athens using deep learning techniques. Heritage, 6(4), 3673-3705. https://doi.org/10.3390/heritage6040195
Bruno, S., Galantucci, R. A., & Musicco, A. (2023). Decay detection in historic buildings through image-based deep learning. VITRUVIO-International Journal of Architectural Technology and Sustainability, 8, 6-17 DOI:10.4995/vitruvioijats.2023.18662
Karimi, N., Valibeig, N., & Rabiee, H. R. (2024). Deterioration detection in historical buildings with different materials based on novel deep learning methods with focusing on isfahan historical bridges. International Journal of Architectural Heritage, 18(6), 981-993. DOI:10.1080/15583058.2023.2201576
Tzortzis, I. N., Rallis, I., Makantasis, K., Doulamis, A., Doulamis, N., & Voulodimos, A. (2022). Automatic inspection of cultural monuments using deep and tensor based learning on hyperspectral imagery. arXiv.
https://doi.org/10.48550/arXiv.2207.02163
Yan, C., Cheng, Y., Zhang, Y., Yu, H., Huang, J., & Yan, H. (2024). Study on the applicability of deterioration detection techniques for sandstone heritage in multi environment using MLP Attention model. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48(4),541–547. https://doi.org/10.5194/isprs-archives-XLVIII-4-2024-541-2024
Alexakis, E., Lampropoulos, K., Doulamis, N., Doulamis, A., & Moropoulou, A. (2022). Deep Learning approach for the identification of Structural Layers in Historic Monuments from Ground Penetrating Radar Images. Scientific Culture, 8(2). DOI:10.5281/zenodo.5772558
Wang, X., Cheng, Y., Zhang, R., Zhang, Y., Huang, J., & Yan, H. (2024). Non destructive assessment of stone heritage weathering types based on machine learning method using hyperspectral data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48(1), 713–719. https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-713-2024
Ramani, P., Reji, V., Sathish Kumar, V., Murali, G., & Kurhade, A. S. (2025). Deep Learning-Based Detection and Classification of Moss and Crack Damage in Rock Structures for Geo-Mechanical Preservation. Journal of Mines, Metals & Fuels, 73(3). https://doi.org/10.18311/jmmf/2025/47760
Perumal, R., & Venkatachalam, S. B. (2023). Non invasive decay analysis of monument using deep learning techniques. Traitement du Signal, 40(2), 639. DOI: https://doi.org/10.18280/ts.400222
Perumal, R., & Venkatachalam, S. B. (2021). RETRACTED ARTICLE: Non invasive detection of moss and crack in monuments using image processing techniques. Journal of Ambient Intelligence and Humanized Computing, 12(5), 5277-5285. 10.1007/s12652-020-02006-x
Pawar, P. Y., & Ainapure, B. S. (2023). Image dataset of Pune city historical places for degradation detection, classification, and restoration. Data in Brief, 51, 109794 doi: 10.1016/j.dib.2023.109794
Rajaramesh, G., Reddy, K. K., Reddy, D. S., & Saiteja, E. (2025). Automated Identification of Indian Heritage Monuments using VGG16-Based Convolutional Neural Networks. Asian Journal of Research in Computer Science, 18(5), 1-10. DOI: 10.9734/ajrcos/2025/v18i5635
Hatır, E., Korkanç, M., Schachner, A., & İnce, İ. (2021). The deep learning method applied to the detection and mapping of stone deterioration in open-air sanctuaries of the Hittite period in Anatolia. Journal of Cultural Heritage, 51, 37-49. https://doi.org/10.1016/j.culher.2021.07.004
Pi, Y., Nath, N. D., & Behzadan, A. H. (2020). Convolutional neural networks for object detection in aerial imagery for disaster response and recovery. Advanced Engineering Informatics, 43, 101009. DOI:10.1016/j.aei.2019.101009
Wang, N., Zhao, X., Wang, L., & Zou, Z. (2019). Novel system for rapid investigation and damage detection in cultural heritage conservation based on deep learning. Journal of Infrastructure Systems, 25(3), 04019020. DOI:10.1061/(ASCE)IS.1943-555X.0000499
Wan, J., Liu, L., Wang, H., Li, L., Li, W., Kou, S., & Hao, R. (2024). UNSCT-HRNet: Modeling Anatomical Uncertainty for Landmark Detection in Total Hip Arthroplasty. arXiv preprint arXiv:2411.08488.
https://doi.org/10.48550/arXiv.2411.08488
Shukla, J. S., & Pandya, R. J. (2023). Deep learning-oriented c-GAN models for vegetative drought prediction on peninsular india. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 282-297. DOI: 10.1109/JSTARS.2023.3328299