AUTOMATIC SURFACE DAMAGE CLASSIFICATION DEVELOPED BASED ON DEEP LEARNING FOR WOODEN ARCHITECTURAL HERITAGE

9 citations as recorded by crossref.

1. Comparison and Interpretability Analysis of Deep Learning Models for Classifying the Manufacturing Process of Pigments Used in Cultural Heritage Conservation I. Go et al. https://doi.org/10.3390/app15073476
2. Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review V. Giannuzzi & F. Fatiguso https://doi.org/10.3390/app14167116
3. An Analysis of Research Trends for Using Artificial Intelligence in Cultural Heritage F. Gîrbacia https://doi.org/10.3390/electronics13183738
4. A Computer Vision and Point Cloud-Based Monitoring Approach for Automated Construction Tasks Using Full-Scale Robotized Mobile Cranes X. Pan et al. https://doi.org/10.26599/JIC.2025.9180086
5. A novel deep neural model for efficient and scalable historical place image classification M. Tasir & M. Khalid https://doi.org/10.1038/s41598-025-26897-y
6. A review of recent advances in data-driven computer vision methods for structural damage evaluation: algorithms, applications, challenges, and future opportunities X. Pan et al. https://doi.org/10.1007/s11831-025-10279-8
7. Intelligent 3D monitoring of historical structures with digital twin R. Helwing & F. Walther https://doi.org/10.1016/j.procs.2026.02.026
8. Investigating and Identifying the Surface Damage of Traditional Ancient Town Residence Roofs in Western Zhejiang Based on YOLOv8 Technology S. Yang et al. https://doi.org/10.3390/coatings15020205
9. Scientific mapping of artificial intelligence (AI) assisted applications in historical building conservation İ. Kutlu https://doi.org/10.1080/13467581.2025.2505794