Satellite derived bathymetry for Arctic charting: a review of sensors and techniques for operational implementation within the Canadian Hydrographic Service
Bibliographic record
Abstract
Canada’s coastline presents challenges for navigational charting. Within Arctic regions, in situ surveying presents risks to surveyors, is time consuming and costly. To better meet its mandate, the Canadian Hydrographic Service (CHS) has been investigating the potential of remote sensing to compliment traditional charting techniques. This paper focuses on an evaluation of sensors and techniques for operational Satellite Derived Bathymetry (SDB) implementation. Analysis focused on Cambridge Bay, Nunavut using Pléiades, SPOT, WorldView and PlanetScope imagery. Multiple SDB techniques were applied to evaluate their agreement with in-situ bathymetric measurements: • An empirical logarithm band ratio approach. • A multiple band modeling technique. • A multi-dimensional Look-Up-Table approach. Through this analysis, CHS attempted to answer critical questions for operational SDB implementation: • Do specific optical sensors offer advantages for SDB? • Are there advantages/disadvantages with the application of SDB techniques within the examined environment? • Can multiple SDB techniques improve CHS’s understanding of the confidence it can place in remotely sensed bathymetry estimates? Early results have achieved overall root mean square errors of 0.56 to 0.99 m relative to in situ survey depths for all sensors and techniques. These similarities suggest that CHS can be confident in the accuracies observed from various SDB approaches. Results do not indicate significant advantages or disadvantages of particular optical sensors, suggesting other factors contain greater importance for SDB image selection (e.g. sea floor visibility). While this analysis provides excellent information for operational empirical SDB implementation within Arctic environments, further work is required within other Canadian coastal regions to support national SDB application.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".