Subsea environments are vital for global biodiversity, climate regulation, and human activities such as fishing, transport, and resource extraction. Accurate mapping and monitoring of these ecosystems are essential for sustainable management. Airborne LiDAR bathymetry (ALB) provides high-resolution underwater data but produces large and complex datasets that make efficient analysis challenging. This study employs deep learning (DL) models for the multi-class classification of ALB waveform data, comparing two recurrent neural networks, i.e., Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM). A preprocessing pipeline was developed to extract and label waveform peaks corresponding to five classes: sea surface, water, vegetation, seabed, and noise. Experimental results from two datasets demonstrated high classification accuracy for both models, with LSTM achieving 95.22% and 94.85%, and BiLSTM obtaining 94.37% and 84.18% on Dataset 1 and Dataset 2, respectively. Results show that the LSTM exhibited robustness and generalization, confirming its suitability for modeling causal, time-of-flight ALB signals. Overall, the findings highlight the potential of DL-based ALB data processing to improve underwater classification accuracy, thereby supporting safe navigation, resource management, and marine environmental monitoring.

Sub-sea environments host rich biodiversity and resources, playing a crucial role in climate regulation while supporting human activities such as fishing, transport, and resource extraction. To maintain healthy underwater ecosystems and ensure sustainable operations, accurate mapping and monitoring are essential. Among available technologies, airborne LiDAR bathymetry (ALB) stands out for its ability to capture detailed subsea data, but handling the enormous datasets it generates remains a major challenge. In this study, we propose a novel preprocessing pipeline combined with deep learning models—Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM)—to classify submerged environments using data from Fjøløy, Stavanger, Norway. LSTM achieved a higher classification accuracy (95.22%) compared to BiLSTM (94.85%). To further assess robustness, classification reliability was evaluated through confidence scores, offering insights into model dependability. The results demonstrate the potential of deep learning for ALB classification and provide practical value for mapping authorities and practitioners in producing reliable seabed maps and nautical charts.