ICA-Proc Proceedings of the ICA ICA-Proc Proc. Int. Cartogr. Assoc. 2570-2092 Copernicus Publications Göttingen, Germany 10.5194/ica-proc-7-16-2025 Urban-Scale Semantic Segmentation Using PointMamba and Mobile Laser Scanning Point Clouds Wu Jiafeng 1 Ma Lingfei 1 Yang Hongxin 1 Li Jonathan 2 School of Geospatial Artificial Intelligence, East China Normal University, Shanghai, China Department of Geography and Environmental Management, University of Waterloo, Waterloo, Canada 21 10 2025 7 16 Copyright: © 2025 Jiafeng Wu et al. 2025 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://ica-proc.copernicus.org/articles/7/16/2025/ica-proc-7-16-2025.html The full text article is available as a PDF file from https://ica-proc.copernicus.org/articles/7/16/2025/ica-proc-7-16-2025.pdf

Point cloud semantic segmentation is a critical task in autonomous driving and digital twin applications. This study introduces a novel semantic segmentation approach leveraging the PointMamba network, specifically designed to address the challenges of complex urban scene point cloud data. The PointMamba network integrates a state space model (SSM) with point cloud serialization and advanced feature extraction techniques, yielding significant performance improvements in semantic segmentation tasks. PointMamba was rigorously evaluated on the Toronto3D urban scene point cloud dataset, achieving an Overall Accuracy (OA) of 93.94% and a mean Intersection over Union (mIoU) of 66.03%. Comparative studies demonstrated that PointMamba outperformed existing point-based methods, including PointNet++ and PointNet, in handling intricate urban environments, delivering superior semantic segmentation outcomes on complex urban road environments.