AN IMAGE-BASED DEEP LEARNING WORKFLOW FOR 3D HERITAGE POINT CLOUD SEMANTIC SEGMENTATION

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1. A Stratigraphic Information System for the Study of Architectural Heritage Assets’ Morphogenesis F. Haouacha et al. 10.1080/15583058.2025.2492239
2. Machine Learning-Based Monitoring for Planning Climate-Resilient Conservation of Built Heritage L. Fiorini et al. 10.3390/drones8060249
3. Optimising 3D point cloud semantic segmentation: ML and manual refinement in the UNESCO Saltaire Village V. Cotella et al. 10.1080/09613218.2025.2521751
4. Point-Cloud Segmentation for 3D Edge Detection and Vectorization T. Betsas & A. Georgopoulos 10.3390/heritage5040208
5. Data Quality, Semantics, and Classification Features: Assessment and Optimization of Supervised ML-AI Classification Approaches for Historical Heritage V. Cera et al. 10.3390/heritage8070265
6. SVD-based point cloud 3D stone by stone segmentation for cultural heritage structural analysis – The case of the Apollo Temple at Delphi D. Galanakis et al. 10.1016/j.culher.2023.04.005
7. Artificial Intelligence at the Interface between Cultural Heritage and Photography: A Systematic Literature Review C. Silva & L. Oliveira 10.3390/heritage7070180
8. An Analysis of Research Trends for Using Artificial Intelligence in Cultural Heritage F. Gîrbacia 10.3390/electronics13183738
9. Automating the retrospective generation of As-is BIM models using machine learning P. Schönfelder et al. 10.1016/j.autcon.2023.104937
10. Instant Segmentation and Fitting of Excavations in Subsurface Utility Engineering M. Stranner et al. 10.1109/TVCG.2024.3372064