TAGNet: A Tidal Flat-Attentive Graph Network Designed for Airborne Bathymetric LiDAR Point Cloud Classification

Airborne LiDAR bathymetry (ALB) provides dense three-dimensional point clouds that enable the detailed mapping of tidal flat environments. However, surface classification using these point clouds remains challenging due to residual noise, water surface reflectivity, and subtle class boundaries that persist even after standard preprocessing. To address these challenges, this study introduces Tidal flat-Attentive Graph Network (TAGNet), a graph-based deep learning framework designed to leverage both local geometric relationships and global contextual cues for the point-wise classification of tidal flat surface classes. The model incorporates multi-scale EdgeConv layers for capturing fine-grained neighborhood structures and employs squeeze-and-excitation channel attention to enhance global feature representation. To validate TAGNet’s effectiveness, classification was conducted on ALB point clouds collected from adjacent tidal flat regions, focusing on four major surface classes: exposed flat, sea surface, sea floor, and vegetation. In benchmarking tests against baseline models, including Dynamic Graph Convolutional Neural Network, PointNeXt with Single-Scale Grouping, and PointNet Transformer, TAGNet consistently achieved higher macro F1-scores. Moreover, ablation studies isolating positional encoding, attention mechanisms, and detrended Z-features confirmed their complementary contributions to TAGNet’s performance. Notably, the full TAGNet outperformed all baselines by a substantial margin, particularly when distinguishing closely related classes, such as sea floor and exposed flat. These findings highlight the potential of graph-based architectures specifically designed for ALB data in enhancing the precision of coastal monitoring and habitat mapping.