AI FOR REGIONAL ART MAPPING AND PRESERVATION

Authors

  • Dr. Peeyush Kumar Gupta Assistant Professor, ISDI - School of Design and Innovation, ATLAS Skilltech University, Mumbai, Maharashtra, India
  • Dr. Amit Kumar Shrivastav Associate Professor, Department of Management, Arka Jain University, Jamshedpur, Jharkhand, India
  • Priya Modi Assistant Professor, Department of Development Studies, Vivekananda Global University, Jaipur, India
  • Fehmina Khalique Greater Noida, Uttar Pradesh 201306, India
  • Nishant Bhardwaj Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India
  • Archana Singh Assistant Professor, School of Sciences, Noida International University 20320, Greater Noida, Uttar Pradesh, India
  • Prashant Anerao Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037 India

DOI:

https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6846

Keywords:

Artificial Intelligence, Cultural Heritage Preservation, Regional Art Mapping, Semantic Ontologies, Cultural Atlas, Explainable AI, Blockchain Provenance, Digital Humanities

Abstract [English]

Artificial intelligence has proven to be a paradigm shift in preservation of cultural heritage and has made it possible to digitize vast portions of heritage, classify intelligently and semantically interconnect regional art forms. The paper introduces a full-fledged AI-based regional art mapping and preservation framework that incorporates various forms of multimodal data visual, textual, and geospatial data in an integrated cultural knowledge framework. The architecture has five layers, including data acquisition, AI analytics, knowledge graph integration, visualization interfaces and ethical governance. The implementation revolves around the Regional Art Knowledge Graph (RAKG) and Cultural Atlas Interface that establishes a semantic and interactive environment of exploring artistic relationships in terms of regions, styles, and time. Technical accuracy (A 1 = 92.3, F1 = 0.91, SSI = 0.84) and cultural authenticity (CAS = 8.7/10, RDI = 0.82) are good. Transparency and contextual fidelity are guaranteed by the presence of explainable AI systems and community engagement systems. The fusion of computational intelligence and human creativity allows the presented system to transform heritage preservation to a dynamic process that is participatory and ties the local traditions to a global digital future.

References

Alizadeh, S. (2017). Cleaning and Restoration of an Oil Painting with a Polymer Gel in Iran. Conservation Science in Cultural Heritage, 17, 139–147.

Agrawal, K., Aggarwal, M., Tanwar, S., Sharma, G., Bokoro, P. N., and Sharma, R. (2022). An Extensive Blockchain-Based Applications Survey: Tools, Frameworks, Opportunities, Challenges and Solutions. IEEE Access, 10, 116858–116906. https://doi.org/10.1109/ACCESS.2022.3219160

Amany, M. K., Gehad, G. M., Niveen, N. K. F., and Ammar, W. B. (2022). Conservation of an Oil Painting from the Beginning of 20th Century. Редакционная Коллегия, 27–31, 315.

Cao, Y., and Scaioni, M. (2021). 3DLEB-Net: Label-Efficient Deep Learning-Based Semantic Segmentation of Building Point Clouds at LoD3 Level. Applied Sciences, 11(19), Article 8996. https://doi.org/10.3390/app11198996

Cao, Y., Teruggi, S., Fassi, F., and Scaioni, M. (2022). A Comprehensive Understanding of Machine Learning and Deep Learning Methods for 3D Architectural Cultural Heritage Point Cloud Semantic Segmentation. In Proceedings of the Italian Conference on Geomatics and Geospatial Technologies (329–341). Springer. https://doi.org/10.1007/978-3-031-17439-1_24

Ferguson, E., Dunne, T., Windrim, L., Bargoti, S., Ahsan, N., and Altamimi, W. (2021). Automated Painting Survey, Degree of Rusting Fication, and Mapping with Machine Learning. In Proceedings of the Abu Dhabi International Petroleum Exhibition and Conference (Paper No. D012S130R001). https://doi.org/10.2118/208119-MS

Gaber, J. A., Youssef, S. M., and Fathalla, K. M. (2023). The Role of Artificial Intelligence and Machine Learning in Preserving Cultural Heritage and Artworks Via Virtual Restoration. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X(1/W1), 185–190. https://doi.org/10.5194/isprs-annals-X-1-W1-2023-185-2023

Gujski, L., di Filippo, A., and Limongiello, M. (2022). Machine Learning Clustering for Point Clouds Optimisation Via Feature Analysis in Cultural Heritage. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVI(2/W1), 245–251. https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-245-2022

Mansuri, L. E., and Patel, D. A. (2022). Current State of Art in Artificial Intelligence and Ubiquitous Cities. Springer.

Musicco, A., Galantucci, R. A., Bruno, S., Verdoscia, C., and Fatiguso, F. (2021). Automatic Point Cloud Segmentation for the Detection of Alterations on Historical Buildings Through an Unsupervised and Clustering-Based Machine Learning Approach. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V(2), 129–136. https://doi.org/10.5194/isprs-annals-V-2-2021-129-2021

Pellis, E., Masiero, A., Tucci, G., Betti, M., and Grussenmeyer, P. (2021). Assembling an Image and Point Cloud Dataset for Heritage Building Semantic Segmentation. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVI(M-1), 539–546. https://doi.org/10.5194/isprs-archives-XLVI-M-1-2021-539-2021

Pellis, E., Murtiyoso, A., Masiero, A., Tucci, G., Betti, M., and Grussenmeyer, P. (2022). An Image-Based Deep Learning Workflow for 3D Heritage Point Cloud Semantic Segmentation. In Proceedings of the 9th International Workshop on 3D-ARCH: 3D Virtual Reconstruction and Visualization of Complex Architectures (426–434). https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-429-2022

Tegmark, M. (2018). Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf Doubleday Publishing Group.

Yu, T., Lin, C., Zhang, S., Wang, C., Ding, X., An, H., Liu, X., Qu, T., Wan, L., and You, S. (2022). Artificial Intelligence for Dunhuang Cultural Heritage Protection: The Project and the Dataset. International Journal of Computer Vision, 130, 2646–2673. https://doi.org/10.1007/s11263-022-01665-x

Zabari, N. (2021). Analysis of Craquelure Patterns in Historical Painting Using Image Processing Along with Neural Network Algorithms. In Proceedings of SPIE: Optics for Arts, Architecture, and Archaeology VIII (Vol. 11784, 21–32). https://doi.org/10.1117/12.2593982

Downloads

Published

2025-12-25

How to Cite

Gupta, P. K., Shrivastav, A. K., Modi, P., Khalique, F., Bhardwaj, N., Singh, A., & Anerao, P. (2025). AI FOR REGIONAL ART MAPPING AND PRESERVATION. ShodhKosh: Journal of Visual and Performing Arts, 6(4s), 400–409. https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6846