A Simplified End-User Approach to Lidar Very Shallow Water Bathymetric Correction
Bibliographic record
Abstract
Airborne lidar bathymetry has been evolving in recent years, and the introduction of multispectral (MS) lidar, such as the Titan sensor from Teledyne Optech, Toronto, ON, Canada, allows for comprehensive mapping of aquatic and riparian areas. However, to derive useful riverbed geomorphology data, bathymetric correction should be applied to raw data based on the proximity of individual points within the laser point cloud to the overlying water surface. Current algorithms within off-the-shelf (OTS) software were developed for flat coastal waters and lakes and, therefore, may have difficulty with shallow river channels possessing discernible surface water gradients and/or complex riparian environments. A proposed simplified correction algorithm is based on a shift (scaling with k = 0.76) in depth values. Riverbed returns are normalized toward the overlying water surface, disregarding laser beam angle of incidence for each point. The resultant simplified bathymetric correction is, therefore, available to most end users and does not rely on black box OTS solutions. Validation of the method with an MS lidar data set is presented, and the expected neglectable introduced bias (sd = 1.4 cm) for very shallow (<; 2 m) water is confirmed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".