TCSF-Net: Transformer-Cloth Simulation Filtering Fusion Network for Semantic Segmentation of Densely Vegetated River Levee LiDAR Point Clouds

Abstract

Accurate semantic segmentation of LiDAR point clouds from urban river levees is critical for flood hazard assessment and infrastructure maintenance, yet dense vegetation cover severely occludes the terrain and introduces ambiguous return signatures, challenging conventional filtering and learning-based methods. To address this, we propose TCSF-Net, a hybrid framework that synergistically integrates a Transformer backbone with a cloth simulation filtering (CSF) prior. The Transformer captures long-range contextual dependencies across irregular point clouds, while the CSF module embeds physical constraints that mimic cloth draping over the surface, guiding the network to preserve terrain continuity and suppress vegetation outliers, especially under canopy occlusion. Experimental results demonstrate that TCSF-Net consistently outperforms state-of-the-art methods, including pure Transformer architectures and classical CSF, in overall accuracy, mean intersection-over-union, and boundary preservation, with marked improvements in sloped transition zones and near-water edges. Our results further show that the fusion of data-driven learning with physically grounded regularization effectively mitigates the ambiguity caused by multi-layer vegetation. We conclude that integrating physical simulation priors into deep segmentation networks offers a robust and generalizable solution for high-fidelity terrain extraction and vegetation discrimination in complex fluvial environments.