Synthetic Sonar Generation: Leveraging Algorithmic and AI-Based Approaches for Enhanced Underwater Mapping and Exploration
Bibliographic record
Abstract
The acquisition of high-quality sonar bathymetry and sidescan imagery is crucial for various underwater applications, including seafloor mapping, habitat characterization, and object detection. However, the collection of such data can be time-consuming, costly, and often limited by environmental and logistical constraints. While synthetic data cannot replace the value of real-world data, it can be a valuable supplement. Specifically, synthetic sonar bathymetry and sidescan imagery can be used to affordably train AI models, facilitate education and training, and enhance the analysis and validation of real data. With these benefits in mind, we propose a novel framework for synthetic generation of sonar bathymetry and sidescan imagery using algorithmic and AI-based tools, which can help augment and support the use of real data in underwater applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".