DEM Manipulation and 3-D Terrain Visualization: Techniques Used by the U.S. National Park Service
Bibliographic record
Abstract
Manipulating digital elevation model (DEM) surfaces, like pliable modelling clay, enhances the appearance and legibility of 3-D topography on maps. The U.S. National Park Service (NPS) uses the familiar image-editing tools in Adobe Photoshop to manipulate raster DEM data. Exporting modified DEM data with the help of freeware and shareware utilities allows subsequent rendering of final 3-D scenes in Corel Bryce. Techniques to be discussed include topographic substitution – a method for reverse engineering present-day landscapes into the past or projecting them into the future; selective vertical exaggeration; resolution bumping – a technique developed specifically for improving the legibility of high-mountain landscapes; painting and filtering effects; and, borrowing from the traditional masters of landform depiction, creating 3-D scenes that emulate the panoramas of Heinrich Berann and the spherical over-the-horizon views of Richard Edes Harrison by warping the projection plane of DEMS. The unique challenges of 3-D mountain mapping and the continuing pursuit of design excellence – a cornerstone of the NPS cartographic program – are overarching themes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".