FROM 2D TO 3D SUPERVISED SEGMENTATION AND CLASSIFICATION FOR CULTURAL HERITAGE APPLICATIONS

24 citations as recorded by crossref.

1. The road toward smart city infrastructures: A review on 3d facade reconstruction using images Y. Arhrib et al. https://doi.org/10.1051/e3sconf/202450202007
2. Semantic Enrichment of Architectural Heritage Point Clouds Using Artificial Intelligence: The Palacio de Sástago in Zaragoza, Spain M. Buldo et al. https://doi.org/10.3390/heritage7120321
3. Transforming Architectural Digitisation: Advancements in AI-Driven 3D Reality-Based Modelling K. Zhang & F. Fassi https://doi.org/10.3390/heritage8020081
4. Image Abstraction Framework as a Pre-processing Technique for Accurate Classification of Archaeological Monuments Using Machine Learning Approaches M. Pavan Kumar et al. https://doi.org/10.1007/s42979-021-00935-8
5. Integration and Comparison Methods for Multitemporal Image-Based 2D Annotations in Linked 3D Building Documentation J. Taraben & G. Morgenthal https://doi.org/10.3390/rs14092286
6. A Multilevel Multiresolution Machine Learning Classification Approach: A Generalization Test on Chinese Heritage Architecture K. Zhang et al. https://doi.org/10.3390/heritage5040204
7. 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. https://doi.org/10.1016/j.culher.2023.04.005
8. The Use of Artificial Intelligence in Building Engineering for Historic Buildings Built in the Austro-Hungarian Monarchy D. Dvornik Perhavec & R. Kamnik https://doi.org/10.1145/3706424
9. Optimising 3D point cloud semantic segmentation: ML and manual refinement in the UNESCO Saltaire Village V. Cotella et al. https://doi.org/10.1080/09613218.2025.2521751
10. Digitizing Temples for heritage conservation: Kalinga Architecture A. Behera & S. Mishra https://doi.org/10.1016/j.daach.2025.e00462
11. Condition Assessment of Heritage Buildings via Photogrammetry: A Scoping Review from the Perspective of Decision Makers M. De Fino et al. https://doi.org/10.3390/heritage6110367
12. Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage R. Pierdicca et al. https://doi.org/10.3390/rs12061005
13. From Symmetry to Semantics: Improving Heritage Point Cloud Classification with a Geometry-Aware, Uniclass-Informed Taxonomy for Random Forest Implementation in Automated HBIM Modelling A. Gil & Y. Arayici https://doi.org/10.3390/sym17101635
14. Application of Deep Learning Approach for the Classification of Buildings’ Degradation State in a BIM Methodology F. Rodrigues et al. https://doi.org/10.3390/app12157403
15. A Stratigraphic Information System for the Study of Architectural Heritage Assets’ Morphogenesis F. Haouacha et al. https://doi.org/10.1080/15583058.2025.2492239
16. Evaluation of Class Distribution and Class Combinations on Semantic Segmentation of 3D Point Clouds With PointNet E. Barnefske & H. Sternberg https://doi.org/10.1109/ACCESS.2022.3233411
17. Reusability and benchmarking potential of architectural cultural heritage datasets for generative AI: An analytical study M. Abu Talib et al. https://doi.org/10.1016/j.eswa.2025.130916
18. The Point Cloud Semantic Segmentation Method for the Ming and Qing Dynasties’ Official-Style Architecture Roof Considering the Construction Regulations Y. Dong et al. https://doi.org/10.3390/ijgi11040214
19. A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification S. Teruggi et al. https://doi.org/10.3390/rs12162598
20. H-BIM and Artificial Intelligence: Classification of Architectural Heritage for Semi-Automatic Scan-to-BIM Reconstruction V. Croce et al. https://doi.org/10.3390/s23052497
21. Machine Learning for the Semi-Automatic 3D Decay Segmentation and Mapping of Heritage Assets R. Galantucci et al. https://doi.org/10.1080/15583058.2023.2287152
22. Automated Mapping of the roof damage in historic buildings in seismic areas with UAV photogrammetry F. Fiorillo et al. https://doi.org/10.1016/j.prostr.2023.01.214
23. A data-driven framework for seismic fragility and performance degradation assessment of historical masonry pagodas: integrating multi-task machine learning and interpretable uncertainty quantification Y. Li et al. https://doi.org/10.1007/s10518-025-02280-0
24. Machine Learning Models for Cultural Heritage Image Classification: Comparison Based on Attribute Selection R. Janković https://doi.org/10.3390/info11010012