Point Cloud Segmentation Based on the Uniclass Classification System with Random Forest Algorithm for Cultural Heritage Buildings in the UK

Bibliographic Data

ID13124674
AuthorsAleksander Gil (0009-0000-6224-8595, Northumbria University), Yusuf Arayıcı (0000-0001-5705-2272, Northumbria University, corresponding author)
Year2025
Volume8
Issue5
Pages147-147
Publication date2025-04-24
Open AccessYes
TypeARTICLE
VenueHeritage (JOURNAL)
Journal identifiersISSN: 2571-9408 • E-ISSN: 2571-9408
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/heritage8050147
OpenAlexW4409736230
LanguageEN
Citations received1
References cited13

This paper presents an advanced hierarchical classification framework using the Random Forest (RF) algorithm to segment and classify large-scale point clouds of heritage buildings. By integrating the Uniclass classification system into a multi-resolution workflow, the research addresses key challenges in point cloud classification, including class imbalance, computational constraints, and semantic overlap at coarse resolutions. It adopts an experimental research design using the heritage case study from Royal Greenwich Museum in the UK. The findings demonstrate that industry classification systems and data taxonomies can be aligned with machine learning workflows. This study contributes to Heritage-Building Information Modelling (HBIM) by proposing optimised hierarchical structures and scalable machine learning techniques. The research concludes with recommendations for future research, based on the performance of the Random Forest technique, particularly in further developing AI applications within HBIM

Algorithm · Archaeology · Cultural heritage · Geography · Geometry · Point (geometry · Point cloud · Random forest · Segmentation · 3D Surveying and Cultural Heritage · Computer Science · Conservation Techniques and Studies · Mathematics · Remote Sensing and LiDAR Applications · Artificial Intelligence

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