A BENCHMARK FOR LARGE-SCALE HERITAGE POINT CLOUD SEMANTIC SEGMENTATION

57 citations as recorded by crossref.

1. GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data R. Pierdicca & M. Paolanti https://doi.org/10.5194/gi-11-195-2022
2. Machine Learning and Deep Learning for Cultural Heritage Conservation: A Bibliometric and Task-Oriented Review X. Li et al. https://doi.org/10.3390/rs18040628
3. A Multilevel Multiresolution Machine Learning Classification Approach: A Generalization Test on Chinese Heritage Architecture K. Zhang et al. https://doi.org/10.3390/heritage5040204
4. Semantic segmentation of point clouds of ancient buildings based on weak supervision J. Zhao et al. https://doi.org/10.1186/s40494-024-01353-8
5. Integrating Inverse Photogrammetry and a Deep Learning–Based Point Cloud Segmentation Approach for Automated Generation of BIM Models Z. Xiang et al. https://doi.org/10.1061/JCEMD4.COENG-13020
6. A Holistic Solution for Supporting the Diagnosis of Historic Constructions from 3D Point Clouds L. Sánchez-Aparicio et al. https://doi.org/10.3390/rs17122018
7. Fast adaptive multimodal feature registration (FAMFR): an effective high-resolution point clouds registration workflow for cultural heritage interiors P. Foryś et al. https://doi.org/10.1186/s40494-023-01018-y
8. Cross modal networks for point cloud semantic segmentation of Chinese ancient buildings Z. Xie et al. https://doi.org/10.1038/s40494-025-01701-2
9. TUM2TWIN: Introducing the large-scale multimodal urban digital twin benchmark dataset O. Wysocki et al. https://doi.org/10.1016/j.isprsjprs.2025.12.013
10. 3DLEB-Net: Label-Efficient Deep Learning-Based Semantic Segmentation of Building Point Clouds at LoD3 Level Y. Cao & M. Scaioni https://doi.org/10.3390/app11198996
11. Semi-automatic classification of digital heritage on the Aïoli open source 2D/3D annotation platform via machine learning and deep learning V. Croce et al. https://doi.org/10.1016/j.culher.2023.05.017
12. Data Preparation Impact on Semantic Segmentation of 3D Mobile LiDAR Point Clouds Using Deep Neural Networks R. Mahmoudi Kouhi et al. https://doi.org/10.3390/rs15040982
13. Learning discriminative universal background knowledge for few-shot point cloud semantic segmentation of architectural cultural heritage R. Liu et al. https://doi.org/10.1038/s40494-025-02292-8
14. From Acquisition to Presentation—The Potential of Semantics to Support the Safeguard of Cultural Heritage J. Ponciano et al. https://doi.org/10.3390/rs13112226
15. DALES Objects: A Large Scale Benchmark Dataset for Instance Segmentation in Aerial Lidar N. Singer & V. Asari https://doi.org/10.1109/ACCESS.2021.3094127
16. DSC-Net: learning discriminative spatial contextual features for semantic segmentation of large-scale ancient architecture point clouds J. Zhao et al. https://doi.org/10.1186/s40494-024-01367-2
17. Transforming Architectural Digitisation: Advancements in AI-Driven 3D Reality-Based Modelling K. Zhang & F. Fassi https://doi.org/10.3390/heritage8020081
18. AI Implementation Roadmap for Automated HBIM: Toward Standardised Digital Workflows for UK Cultural Heritage A. Gil & Y. Arayici https://doi.org/10.3390/buildings16050921
19. Data Quality, Semantics, and Classification Features: Assessment and Optimization of Supervised ML-AI Classification Approaches for Historical Heritage V. Cera et al. https://doi.org/10.3390/heritage8070265
20. Mapping heritage digitalization research through openAlex: a large-scale scientometric analysis with hybrid semantic classification K. Lipianina-Honcharenko https://doi.org/10.1007/s41060-026-01213-7
21. Enhancing explainability of deep learning models for point cloud analysis: a focus on semantic segmentation F. Matrone et al. https://doi.org/10.1080/17538947.2024.2390457
22. Semantic Mapping of Landscape Morphologies: Tuning ML/DL Classification Approaches for Airborne LiDAR Data M. Cappellazzo et al. https://doi.org/10.3390/rs16193572
23. Public Building Geometric Models From Point Clouds: A multidimensional quality evaluation framework D. Chen et al. https://doi.org/10.1109/MGRS.2025.3618468
24. Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds F. Matrone & M. Martini https://doi.org/10.4995/var.2021.15318
25. Machine Learning and Deep Learning for the Built Heritage Analysis: Laser Scanning and UAV-Based Surveying Applications on a Complex Spatial Grid Structure D. Billi et al. https://doi.org/10.3390/rs15081961
26. MBHI: An explainable deep learning framework for interpreting multi-scale regional characteristics of built heritage from point clouds X. Zu et al. https://doi.org/10.1016/j.foar.2026.04.015
27. Development of historic building information modelling: a systematic literature review S. Bastem & A. Cekmis https://doi.org/10.1080/09613218.2021.1983754
28. From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning V. Croce et al. https://doi.org/10.3390/rs13030461
29. Shaping Digital Twin Concept for Built Cultural Heritage Conservation: A Systematic Literature Review A. Vuoto et al. https://doi.org/10.1080/15583058.2023.2258084
30. City-Facade: A city-level large-scale point cloud building facade dataset for semantic & instance segmentation Y. Chen et al. https://doi.org/10.1016/j.isprsjprs.2026.01.003
31. Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry A. Murtiyoso et al. https://doi.org/10.3390/s22030966
32. Synthetic Training Datasets for Architectural Conservation: A Deep Learning Approach for Decay Detection G. Patrucco et al. https://doi.org/10.3390/rs17101714
33. Structure- and Semantics-Aware Mesh Simplification for Generating Lightweight 3D Building Models D. Chen et al. https://doi.org/10.3390/rs18060914
34. A methodological proposal for supporting structural assessment of historic timber floors based on terrestrial laser scanner point clouds P. Villanueva-Llauradó et al. https://doi.org/10.1016/j.cscm.2025.e04978
35. A Review of Software Solutions to Process Ground-based Point Clouds in Forest Applications A. Murtiyoso et al. https://doi.org/10.1007/s40725-024-00228-2
36. Deep Learning for LiDAR Point Cloud Classification in Remote Sensing A. Diab et al. https://doi.org/10.3390/s22207868
37. 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
38. FTG-Net: A facade topology-aware graph network for class imbalance structural segmentation of building facades Y. Zang et al. https://doi.org/10.1016/j.isprsjprs.2025.07.014
39. Spatial Analysis of Urban Historic Landscapes Based on Semiautomatic Point Cloud Classification with RandLA-Net Model—Taking the Ancient City of Fangzhou in Huangling County as an Example J. Wang et al. https://doi.org/10.3390/land14061156
40. 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
41. Towards trustworthy AI in cultural heritage M. Paolanti et al. https://doi.org/10.1038/s40494-026-02403-z
42. Unsupervised Dual Transformer Learning for 3-D Textured Surface Segmentation I. Ganapathi et al. https://doi.org/10.1109/TNNLS.2024.3365515
43. Automated heritage building component recognition and modelling based on local features B. Pang et al. https://doi.org/10.1016/j.culher.2024.12.006
44. Segmentation of 3D Point Clouds of Heritage Buildings Using Edge Detection and Supervoxel-Based Topology S. Salamanca et al. https://doi.org/10.3390/s24134390
45. A Semi-Automatic Annotation Methodology for Photogrammetric Images of Heritage Buildings E. Pellis et al. https://doi.org/10.1080/15583058.2025.2586029
46. A Deep Learning Multiview Approach for the Semantic Segmentation of Heritage Building Point Clouds E. Pellis et al. https://doi.org/10.1080/15583058.2025.2485242
47. DGPCD: a benchmark for typical official-style Dougong in ancient Chinese wooden architecture C. Zhou et al. https://doi.org/10.1186/s40494-024-01293-3
48. A TLS-Based Framework for the Realization of Digital Twin Basemaps Applied to an Adaptively Reused Heritage Building M. Salaheldin et al. https://doi.org/10.3390/app16168306
49. Procedural modeling of historic buildings' timber frames for HBIM based on carpenters' architectural rules F. Xu et al. https://doi.org/10.1016/j.autcon.2024.105862
50. Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies A. Klimkowska et al. https://doi.org/10.3390/rs14112579
51. Deep Learning on 3D Semantic Segmentation: A Detailed Review T. Betsas et al. https://doi.org/10.3390/rs17020298
52. BubblEX: An Explainable Deep Learning Framework for Point-Cloud Classification F. Matrone et al. https://doi.org/10.1109/JSTARS.2022.3195200
53. Key Point Identification in Deep Learning-Based 3D Semantic Segmentation M. Ferrandon Cervantes & A. Reiterer https://doi.org/10.1007/s41064-026-00387-w
54. Semantic-guided 3D building reconstruction from triangle meshes S. Wang et al. https://doi.org/10.1016/j.jag.2023.103324
55. ML Approaches for the Study of Significant Heritage Contexts: An Application on Coastal Landscapes in Sardinia M. Cappellazzo et al. https://doi.org/10.3390/heritage7100261
56. Weakly supervised 3D point cloud semantic segmentation for architectural heritage using teacher-guided consistency and contrast learning S. Huang et al. https://doi.org/10.1016/j.autcon.2024.105831
57. Digital Restoration of Cultural Heritage With Data-Driven Computing: A Survey A. Basu et al. https://doi.org/10.1109/ACCESS.2023.3280639