Satellite-Derived Bathymetry Combined With Sentinel-2 and ICESat-2 Datasets Using Deep Learning
Bibliographic record
Abstract
Accurate bathymetric data is critical for marine ecological balance and resource management. Deep learning algorithms, known for their capacity to model complex, multivariate, and nonlinear relationships, have been increasingly applied to satellite-derived bathymetry (SDB). However, existing deep learning models are limited by simple architectures and low efficiency in hyperparameter optimization, resulting in suboptimal training performance. This paper proposes a Convolutional Neural Network and Bidirectional Long Short-Term Memory hybrid model based on the Bayesian Optimization Algorithm (BOA-CNN-BILSTM) to enhance bathymetric inversion accuracy and efficiency. The model employs BOA to optimize the key hyperparameters of the CNN-BILSTM architecture, thereby improving inversion performance. Bathymetric inversion experiments were conducted using fused ICESat-2 and Sentinel-2 data, focusing on Coral Island and Dong Island in the South China Sea, as well as Midway Island and Oahu Island in the Pacific Ocean. Comparative experiments demonstrated that BOA significantly outperforms conventional random search (RS) by achieving near-optimal hyperparameter configurations with fewer evaluations, accelerating convergence and reducing computational costs. The BOA-CNN-BILSTM model reduced the root mean square error (RMSE) by 28.6%–56.5%, 29.6%–53.7%, 34.1%–52.6%, and 28.9%–57.1% across the study areas compared with the Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BILSTM), and CNN-BILSTM models. Other evaluation metrics also showed varying degrees of improvement. These results demonstrate that the proposed approach is effective and highly accurate for bathymetric inversion in shallow waters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".