Bathymetric Photogrammetry to Update CHS Charts: Comparing Conventional 3D Manual and Automatic Approaches
Bibliographic record
Abstract
The Canadian Hydrographic Service (CHS) supports safe navigation within Canadian waters through approximately 1000 navigational charts as well as hundreds of publications. One of the greatest challenges faced by the CHS is removing gaps in bathymetric survey data, particularly in the Canadian Arctic where only 6% of navigational water is surveyed to modern standards. Therefore, the CHS has initiated a research project to explore remote sensing methods to improve Canadian navigational charts. The major components of this project explore satellite derived bathymetry (SDB), coastline change detection and coastline extraction. This paper focuses on the potential of two stereo satellite techniques for deriving SDB: (i) automatic digital elevation model (DEM) extraction using a semi-global matching method, and (ii) 3D manual delineation of depth contours using visual stereoscopic interpretation. Analysis focused on quantitative assessment which compared estimated depths from both automatic and 3D manual photogrammetric approaches against available in situ survey depths. The results indicate that the 3D manual approach provides an accuracy of <2 m up to a depth of 15 m. Comparable results were obtained from the automatic approach to a depth of 12 m. For almost all investigated depth ranges for both techniques, uncertainties were found to be within the required vertical accuracies for the International Hydrographic Organization category zone of confidence (CATZOC) level C classification for hydrographic surveys. This indicates that both techniques can be used to derive navigational quality bathymetric information within the investigated study site. While encouraging, neither technique was found to offer a single solution for the complete estimation of depth within the study area. As a result of these findings, the CHS envisions a hybrid approach where stereo- and reflectance-based bathymetry estimation techniques are implemented to provide the greatest understanding of depth possible from satellite imagery. Overall, stereo photogrammetry techniques will likely allow for new potential for supporting the improvement of CHS charts in areas where modern surveys have not yet been obtained.
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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 | 0.001 |
| 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 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".