ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-XII-4-W1-2026-1-2026 A Comparative Analysis of Four Semantic Segmentation Models for Classification of Building Structural Elements in Highly Occluded Environments Akhoundi Khezrabad Mojtaba https://orcid.org/0000-0003-0325-4359 1 Shojaei Davood 1 Tomko Martin 2 Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC 3053, Australia Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC 3053, Australia 28 09 2026 XII-4/W1-2026 1 8 Copyright: © 2026 Mojtaba Akhoundi Khezrabad et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/XII-4-W1-2026/1/2026/isprs-annals-XII-4-W1-2026-1-2026.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/XII-4-W1-2026/1/2026/isprs-annals-XII-4-W1-2026-1-2026.pdf

Semantic segmentation of point clouds plays a critical role in automatic Scan-to-BIM workflows. This study evaluates the performance of two Multi-Layer-Perceptron (MLP)-based deep learning networks (PointNet++, PointNeXt-XL) and two transformer-based networks (Point Transformer V1 (PTv 1), Point Transformer V3 (PTv 3)) for the identification of building structural elements in highly occluded environments. The models are trained and tested on three LiDAR datasets of reinforced concrete structures including office buildings and a multi-storey carpark. Results show that transformer-based networks significantly outperform MLP-based architectures under heavy occlusions. Within the transformer-based models, PTv 1 achieved the highest Overall Accuracy (OA) at 92.62% while PTv 3 provided the best balance across all classes, particularly for beam and clutter classes due to its larger receptive field and enhanced geometric encoding. Because segmentation approaches can compensate for errors in ceiling, floor, and column class identification, we recommend PTv 3 for Scan-to-BIM applications focused on structural modelling.