Assessment of Rock and Stone Decay in Heritage Sites Using Machine Learning
Cultural heritage sites face growing threats from environmental factors and human activities, highlighting the need for efficient techniques to monitor and preserve their structural integrity. While advanced machine learning models, such as Segment Anything Model (SAM), have shown success in areas s...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-07-01
|
| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-G-2025/1019/2025/isprs-annals-X-G-2025-1019-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839629756301049856 |
|---|---|
| author | W. Ying K. Khoshelham J. Kemp |
| author_facet | W. Ying K. Khoshelham J. Kemp |
| author_sort | W. Ying |
| collection | DOAJ |
| description | Cultural heritage sites face growing threats from environmental factors and human activities, highlighting the need for efficient techniques to monitor and preserve their structural integrity. While advanced machine learning models, such as Segment Anything Model (SAM), have shown success in areas such as healthcare, their potential for cultural heritage conservation remains largely unexplored. In this research, we propose an automatic decay detection and visualization framework by combining advanced segmentation techniques with 3D reconstruction methods. We fine-tune SAM and integrate it with You Only Look Once (YOLO) to create a fully automatic, real-time segmentation framework that offers strong generalization for identifying unseen decay types. By incorporating Structure from motion (SfM) and multi-view stereo (MVS), the framework produces 3D models that highlight decay regions, providing a robust tool for structural assessment and visualization. Through both quantitative and qualitative evaluations, we show that our approach outperforms several state-of-the-art models, demonstrating its effectiveness in identifying and visualizing stone decay. Our results contributes to heritage preservation by providing a novel, scalable solution for real-time monitoring of cultural heritage sites. |
| format | Article |
| id | doaj-art-a84801ca9f084f82a8c693b88bc577e3 |
| institution | Matheson Library |
| issn | 2194-9042 2194-9050 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-a84801ca9f084f82a8c693b88bc577e32025-07-14T21:08:15ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502025-07-01X-G-20251019102610.5194/isprs-annals-X-G-2025-1019-2025Assessment of Rock and Stone Decay in Heritage Sites Using Machine LearningW. Ying0K. Khoshelham1J. Kemp2School of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, AustraliaDepartment of Infrastructure Engineering, The University of Melbourne, Melbourne, Victoria, AustraliaGrimwade Centre for Cultural Materials Conservation, The University of Melbourne, Melbourne, Victoria, AustraliaCultural heritage sites face growing threats from environmental factors and human activities, highlighting the need for efficient techniques to monitor and preserve their structural integrity. While advanced machine learning models, such as Segment Anything Model (SAM), have shown success in areas such as healthcare, their potential for cultural heritage conservation remains largely unexplored. In this research, we propose an automatic decay detection and visualization framework by combining advanced segmentation techniques with 3D reconstruction methods. We fine-tune SAM and integrate it with You Only Look Once (YOLO) to create a fully automatic, real-time segmentation framework that offers strong generalization for identifying unseen decay types. By incorporating Structure from motion (SfM) and multi-view stereo (MVS), the framework produces 3D models that highlight decay regions, providing a robust tool for structural assessment and visualization. Through both quantitative and qualitative evaluations, we show that our approach outperforms several state-of-the-art models, demonstrating its effectiveness in identifying and visualizing stone decay. Our results contributes to heritage preservation by providing a novel, scalable solution for real-time monitoring of cultural heritage sites.https://isprs-annals.copernicus.org/articles/X-G-2025/1019/2025/isprs-annals-X-G-2025-1019-2025.pdf |
| spellingShingle | W. Ying K. Khoshelham J. Kemp Assessment of Rock and Stone Decay in Heritage Sites Using Machine Learning ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Assessment of Rock and Stone Decay in Heritage Sites Using Machine Learning |
| title_full | Assessment of Rock and Stone Decay in Heritage Sites Using Machine Learning |
| title_fullStr | Assessment of Rock and Stone Decay in Heritage Sites Using Machine Learning |
| title_full_unstemmed | Assessment of Rock and Stone Decay in Heritage Sites Using Machine Learning |
| title_short | Assessment of Rock and Stone Decay in Heritage Sites Using Machine Learning |
| title_sort | assessment of rock and stone decay in heritage sites using machine learning |
| url | https://isprs-annals.copernicus.org/articles/X-G-2025/1019/2025/isprs-annals-X-G-2025-1019-2025.pdf |
| work_keys_str_mv | AT wying assessmentofrockandstonedecayinheritagesitesusingmachinelearning AT kkhoshelham assessmentofrockandstonedecayinheritagesitesusingmachinelearning AT jkemp assessmentofrockandstonedecayinheritagesitesusingmachinelearning |