APPLICATION OF MACHINE AND DEEP LEARNING STRATEGIES FOR THE CLASSIFICATION OF HERITAGE POINT CLOUDS

44 citations as recorded by crossref.

1. A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification S. Teruggi et al.
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11. Measuring rock slope damage on rubble mound breakwater through digital photogrammetry S. Marino et al.
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18. Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review S. Yang et al.
19. Aerial Point Cloud Classification Using an Alternative Approach for the Dynamic Computation of K-Nearest Neighbors I. Pârvu et al.
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23. A Multilevel Multiresolution Machine Learning Classification Approach: A Generalization Test on Chinese Heritage Architecture K. Zhang et al.
24. Application of Deep Learning Approach for the Classification of Buildings’ Degradation State in a BIM Methodology F. Rodrigues et al.
25. Two-Dimensional Shape Analysis of Complex Geometry Based on Photogrammetric Models of Iconostases I. Đurić et al.
26. The Point Cloud Semantic Segmentation Method for the Ming and Qing Dynasties’ Official-Style Architecture Roof Considering the Construction Regulations Y. Dong et al.
27. Advancements in portable mobile mapping system point-cloud classification: a user-centric comparison of open-source and commercial machine learning and deep learning solutions M. Previtali et al.
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29. Convolutional Neural Networks and Regression Algorithms Supporting Buildings Facility Management R. Matos et al.
30. Research Ancient Artifact Identification Methods under Intelligent Perception and Recognition Technology Q. Zhao & V. Kumaravelu
31. Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review M. Mekala et al.
32. Deep learning methods applied to digital elevation models: state of the art J. Ruiz-Lendínez et al.
33. Enhancing preservation outcomes for architectural heritage buildings through machine learning-driven future search optimization S. Abusaleh
34. An Open System for Collection and Automatic Recognition of Pottery through Neural Network Algorithms M. Gualandi et al.
35. Metodología de captura y procesado para el desarrollo de una base de datos gráfica 3D del patrimonio arquitectónico M. Quintilla Castán & L. Agustín Hernández
36. Deep learning based approaches from semantic point clouds to semantic BIM models for heritage digital twin X. Pan et al.
37. Transforming Architectural Digitisation: Advancements in AI-Driven 3D Reality-Based Modelling K. Zhang & F. Fassi
38. AI Implementation Roadmap for Automated HBIM: Toward Standardised Digital Workflows for UK Cultural Heritage A. Gil & Y. Arayici
39. Optimising 3D point cloud semantic segmentation: ML and manual refinement in the UNESCO Saltaire Village V. Cotella et al.
40. 3D Documentation with TLS of Caliphal Gate (Ceuta, Spain) R. Pérez-Álvarez et al.
41. Machine Learning for the Semi-Automatic 3D Decay Segmentation and Mapping of Heritage Assets R. Galantucci et al.
42. A Semi-Automatic Annotation Methodology for Photogrammetric Images of Heritage Buildings E. Pellis et al.
43. Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation F. Matrone et al.
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