AUTOMATIC HERITAGE BUILDING POINT CLOUD SEGMENTATION AND CLASSIFICATION USING GEOMETRICAL RULES

10 citations as recorded by crossref.

1. Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage R. Pierdicca et al. 10.3390/rs12061005
2. Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements A. Murtiyoso & P. Grussenmeyer 10.3390/s20082161
3. Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds F. Matrone & M. Martini 10.4995/var.2021.15318
4. A Review of Software Solutions to Process Ground-based Point Clouds in Forest Applications A. Murtiyoso et al. 10.1007/s40725-024-00228-2
5. Multi-scale neighborhood selection network for the Great Wall point cloud segmentation L. Wang et al. 10.1038/s40494-025-01545-w
6. Segmentation of Change in Surface Geometry Analysis for Cultural Heritage Applications S. Saha et al. 10.3390/s21144899
7. Deep learning methods applied to digital elevation models: state of the art J. Ruiz-Lendínez et al. 10.1080/10106049.2023.2252389
8. Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry A. Murtiyoso et al. 10.3390/s22030966
9. 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
10. Systematic review: a bibliometric analysis of building technology and its potential applications to artificial intelligence in the field of cultural heritage conservation from 2013 to 2023 B. Li et al. 10.1080/13467581.2025.2467251