Where land meets sea: towards a complete coverage for terrain analysis of coastal environments
Bibliographic record
Abstract
Developments in remote sensing techniques to produce digital terrain models (DTM) have provided new methods for studying terrestrial environments, thereby improving scientific understanding of ecological and geomorphological processes. Geomorphometry, or terrain analysis, uses DTMs to quantify physical characteristics of the land (e.g., slope or rugosity) that are used for different applications such as habitat mapping. The use of multibeam echosounder systems (MBES) to measure seafloor relief has changed the way we study and understand marine environments; techniques from geomorphometry are now commonly applied to these underwater DTMs for the investigation of marine habitats and geomorphology.<p> </p>Due to the inability of satellite remote sensing to collect data in deep waters and the limitations of MBES data collection in shallower waters, there is often a gap in terrain data where land meets sea. A seamless analysis of terrestrial and marine environments requires the combination of terrestrial DTMs, bathymetric data from MBES, and bathymetric LiDAR to fill this gap. The challenges encountered with merging datasets from different sources, such as data uncertainty or spatial resolution, makes such an approach still nascent in the literature.<p> </p>Using data from coastal Newfoundland and Labrador, this contribution reviews the potential uses of continuous terrestrial/marine terrain models, especially for the field of geomorphometry and its application in coastal environments. We argue that such DTMs can become essential to applications like coastal habitat mapping, identification of hazards for navigation in shallow waters, or studying landforms that overlap between environments. We also discuss how some techniques traditionally used in terrestrial studies, such as Geographic object-based image analysis (Geobia), can be applied in marine studies.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".