A Simplification of Ria Coastline with Geomorphologic Characteristics Preserved
Bibliographic record
Abstract
To meet the requirements of multi-scale mapping in maritime applications, marine charts need to be produced at various levels of detail (LOD) using map generalization. As a prominent geographic feature, the coastline has to be generalized considering the geomorphologic characteristics rather than from a pure geometric perspective. Morphologic and domain-specific constraints (e.g., safety) should be incorporated in designing a coastline generalization algorithm. Motivated by the generalization of ria coastlines, this article proposes a simplification algorithm that is specific to coastlines. An analysis of ria coasts results in several morphologic constraints that have to be satisfied in coastline generalization, such as the dendritic pattern of estuaries. To satisfy these constraints, a hierarchical estuary tree model is first established by Delaunay triangulation, which helps to represent the dendritic pattern of ria coastlines. Minor estuaries are then deleted to achieve a reasonable coastline simplification. To imitate manual generalization, an indicator is designed to calculate the importance of estuaries in a context dependent manner. By comparing with a well-known bend simply algorithm, we show that the presented method can maintain dendritic pattern of coastline and is free from self-intersection and also granted for navigation safety. This article also demonstrates that the proposed approach is applicable to coastlines in general.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".