Automated Reduction of Visual Complexity in Small-Scale Relief Shading
Bibliographic record
Abstract
Shaded relief derived automatically from digital elevation models differs distinctly from traditional manual shading. Particularly at small scales, many small topographic details that are present in terrain models disturb the clear portrayal of the main relief features. Automatic shading is therefore not appropriate for high-quality cartographic products. This paper proposes a new method of generalizing digital elevation models for deriving small-scale shaded relief that resembles the manual style. The procedure consists of the following raster operations: undesirable topographic details are smoothed with low-pass filters, and the main landforms, such as ridgelines and valleys, are detected by curvature coefficients. Two secondary grids are derived, one exaggerating ridgelines, the other deepening valley bottoms, and the two grids are combined according to the character of the terrain; the grid with exaggerated ridgelines is used in mountainous areas, and the grid with deepened valley bottoms in lowland areas. Finally, shaded relief is derived from the combined elevation model. Following these processing steps, only a few manual corrections are necessary to produce high-quality small-scale relief shading.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".