A Segmentation Approach to Identify Underwater Dunes from Digital Bathymetric Models
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Bibliographic record
Abstract
The recognition of underwater dunes has a central role to ensure safe navigation. Indeed, the presence of these dynamic landforms on the seafloor represents a hazard for navigation, especially in navigation channels, and should be at least highlighted to avoid collision with vessels. This paper proposes a novel method dedicated to the segmentation of these landforms in the fluvio-marine context. Its originality relies on the use of a conceptual model in which dunes are characterized by three salient features, namely the crest line, the stoss trough, and the lee trough. The proposed segmentation implements the conceptual model by considering the DBM (digital bathymetric model) as the seafloor surface from which the dunes shall be segmented. A geomorphometric analysis of the seabed is conducted to identify the salient features of the dunes. It is followed by an OBIA (object-based image analysis) approach aiming to eliminate the pixel-based analysis of the seabed surface, forming objects to better describe the dunes present in the seafloor. To validate the segmentation method, more than 850 dunes were segmented in the fluvio-marine context of the Northern Traverse of the Saint-Lawrence river. A performance rate of nearly 92% of well segmented dunes (i.e., true positive) was achieved.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it