Satellite-Derived Bathymetry Using Machine Learning Methods
Bibliographic record
Abstract
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 environments. The models were trained on data from several Canadian sites, and their transferability was tested on unseen sites. DL models, U-Net, SegNet and DeepLabv3 +, achieved about two times higher F1 scores than traditional methods. From the class-wise scores, ML/DL models can reliably predict up to a depth of 8m in freshwater, due to greater water transparency, and up to 3m in saltwater, due to sediments that prevent light penetration. Traditional methods struggled in deeper and turbid waters, but performed similarly to the machine learning Random Forest model in both environments. Overall, the ML/DL models have difficulties generalizing to unseen data or data from geographic locations not included in the training process, particularly in saltwater due to poor light penetration. 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 over traditional methods while identifying challenges in model generalization and data diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".