Machine learning approaches for satellite-derived bathymetry in tropical coastal waters: A comparative study from Nha Trang marine protected area, Vietnam
Bibliographic record
Abstract
Bathymetry mapping plays a critical role in coastal zone management, marine conservation, and navigation safety. With the increasing availability of high-resolution satellite imagery, such as PlanetScope (3−5 m), remote sensing-based bathymetry retrieval offers a cost-effective and scalable alternative to traditional in-situ surveys. This study explores the capability of PlanetScope imagery to retrieve a wide range of bathymetry (-0.5 − ~ -40 m) in the southern area of the Nha Trang Marine Protected Area (MPA), Vietnam - an ecologically significant and dynamic coastal region. We conduct a comprehensive comparison between traditional approaches, including the Stumpf ratio model and Multiple Linear Regression (MLR), and a suite of advanced machine learning (ML) algorithms, including Random Forest (RF), Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGB), CatBoost (CB), and Gradient Boosting (GB). Among these, RF achieved the highest performance with an R2 of 0.85, RMSE of 2.66 m, and MAE of 1.85 m, significantly outperforming the Stumpf model (R2 = 0.29) and MLR (R2 = 0.57). This study represents one of the most extensive model comparisons to date for satellite-derived bathymetry using PlanetScope data, offering a benchmark for future applications in tropical coastal environments. Results underscore the potential of machine learning to advance spatially detailed and accurate bathymetric mapping from space.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".