Bibliographic record
Abstract
Sky models are quantitative representations of natural luminance of the sky under various atmospheric conditions. They have been used extensively in studies of architectural design for nearly a century, and more recently for rendering objects in the field of computer graphics. The objectives of this paper are to (1) describe sky models, (2) demonstrate how map designers can render terrain under various sky models in a typical geographic information system (GIS), (3) illustrate potential enhancements to terrain renderings using sky models, and (4) discuss how sky models, with their well-established standards from a different discipline, might contribute to a virtual geographic environment (VGE).Current GIS hill-shading tools use the Lambertian assumption which can be related to a simple point light source at an infinite distance to render terrain. General sky models allow the map designer to choose from a gamut of sky models standardized by the International Commission on Illumination (CIE). We present a computer application that allows the map designer to select a general sky model and to use existing GIS tools to illuminate any terrain under that model. The application determines the orientations and weights of many discrete point light sources that, in the aggregate, approximate the illumination provided by the chosen sky model. We discuss specific enhancements to terrains that are shaded and shadowed with these general sky models, including additional detail of secondary landforms with soft shadows and more realistic shading contrasts. We also illustrate how non-directional illumination models result in renderings that lack the perceptual relief effect. Additionally, we argue that this process of creating hill-shaded visualizations of terrain with sky models shows parallels to other geo-simulations, and that basing such work on standards from the computer graphics industry shows potential for its use in VGE.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".