Multi-branch Deep Learning Architecture for bathymetric LiDAR Point Cloud Classification

Abstract. Accurate classification of topo-bathymetric LiDAR data remains challenging due to the heterogeneous nature of land-water transitional environments, where terrestrial, water surface, and submerged features must be distinguished simultaneously. This study presents a multi-branch deep learning architecture for classifying bathymetric LiDAR data into different classes: soil ground, trees and vegetation, water surface, seabed, aquatic plants and other underwater objects (dead wood, coral reef). The proposed framework employs three parallel feature extraction branches, while the first branch captures spatial structure by focusing on three-dimensional geometric coordinates (XYZ), the other two branches use two independent 1D U-Net architectures to extract signal-based features from RGB spectral reflectance and waveform-derived attributes (intensity, return number, number of returns). The discrete LiDAR attributes, though represented as point-wise numerical values, preserve signal characteristics derived from full-waveform analysis. The encoder-decoder of 1D U-Net architecture with skip connections effectively captures sequential patterns and multi-return patterns in different classes especially in vegetation canopies. The three feature streams are fused through fully-connected layers before final classification. Evaluation using different metrics demonstrates the capability of the framework to simultaneously classify diverse coastal zone and inland waters contexts spanning terrestrial and submerged domains within a unified processing pipeline, eliminating the need for separate terrestrial and bathymetric classification workflows.