Thirty years of satellite derived bathymetry – The charting tool that hydrographers can no longer ignore
Bibliographic record
Abstract
Thirty years after being introduced into national chart series, Satellite Derived Bathymetry (SDB) charts are still struggling to be recognised as valid navigation documents, capable of meeting the level of confidence required by the S-44 IHO standards for hydrographic surveys. The advent of new generation satellite constellations, such as Sentinel-2*, provide improved geolocation and, thanks to higher revisit frequency, an almost unlimited capacity to detect natural dangers visible from space within the limits of the sensing instruments. Thus, this negative vision of SDB must change. Written by Hydrographers, this article aims to provide a scientific background adapted to practical Hydrography; introduce the notion of “Perfect Image”, first mentioned at the International Hydrographic Remote Sensing workshop (Ottawa, September 2018); and rehabilitate older concepts such as Depth of Penetration (DOP), which make SDB an incomparable instrument to chart the World’s shallow waters (Fig. 1). Here, “incomparable” does not mean “perfect”, as there are limits to SDB capacity to detect and quantify bottom structures that will be detailed later. *The frequent mention of Sentinel-2 should not lead the reader to believe that the authors are focussing on this constellation. The intention is to show how satellite hydrography has evolved naturally from exploiting unique images to processing large collections that provide ever-improved information, the latest example happening to be Sentinel-2.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".