Machine and deep learning methods for satellite-derived bathymetric mapping in Canadian coastal waters

The performance of machine and deep learning (ML/DL) classification models is evaluated for depth range mapping in shallow freshwater and saltwater coastal environments in Canada using satellite-derived bathymetry (SDB). The models were trained on data from several Canadian sites, and their transferability was tested on geographically distinct, unseen sites. When compared to depth ranges derived from empirical and physics methods, the DL models, including U-Net, SegNet, and DeepLabv3+ achieved more than twice the F1 scores of in saltwater and freshwater environments, such as Graham Island and Athol, with more modest improvements observed in mixed or complex water types, such as Rimouski. From the class-wise scores, ML/DL models can moderately predict depth ranges up to 8 m in freshwater due to greater water transparency, and up to 3 m depth ranges in saltwater, where suspended sediments limit light penetration. Traditional methods struggled to derived water depth classes in deeper and turbid waters but performed similarly to the machine learning Random Forest classifier model in both environments. In addition to the classification performance metrics (precision, recall, F1), visual assessment of the predicted bathymetric maps showed that DL models captured shoreline features, water depth gradients, and seafloor morphology more accurately, especially in shallow waters. In general, the ML/DL models faced challenges in unseen geographic domains in the 2-8 m water depth classes. Overall, this study highlights the potential of machine learning and deep learning over traditional methods while also emphasizing the need for diverse training data improvements in model transferability. • Satellite-derived bathymetry (SDB) modelling is a relatively novel technique for surveying shallow coastal zones. It involves passive remote sensing techniques to estimate water depth from satellite imagery. According to our literature research, there is ongoing research in SDB using data-driven models in tropical and subtropical regions; however, there is limited work in higher-latitude regions beyond a latitude of 35°. • This study evaluates the performance of machine and deep learning (ML/DL) models for satellite-derived bathymetry (SDB) compared to traditional empirical and physics-based methods in high-latitude freshwater and saltwater coastal environments in Canada. • DL models, including U-Net, SegNet, and DeepLabv3+, achieved approximately twice the F1 scores of traditional methods. • The depth results met the Zones of Confidence (ZOC) categories, CATZOC C and CATZOC D, of the International Hydrographic Organization (IHO) depth accuracy standards. • Overall, this study highlights the advantages of machine learning and deep learning over traditional methods while identifying challenges in model generalization and data diversity.

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