Work

Applying Deep Learning to Bathymetric LiDAR Point Cloud Data for Classifying Submerged Environments

Year: 2025

Type: article

Abstract: 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.

Source: Applied Sciences

DOI: 10.3390/app152412914

Authors: Nabila Tabassum, Henri Giudici, Vimala Nunavath, Ivar Oveland

Institutions: University of South-Eastern Norway, Norwegian Mapping Authority

Language: English

Topic: Remote Sensing and LiDAR Applications

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Life below water

Open Access status: gold

Estimated APC paid: $2,896