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Record W2086243512 · doi:10.1080/13658816.2013.848985

General sky models for illuminating terrains

2013· article· en· W2086243512 on OpenAlex
Patrick J. Kennelly, A. James Stewart

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Geographical Information Systems · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsQueen's University
Fundersnot available
KeywordsSkyTerrainGeographyCartographyRemote sensingComputer scienceData scienceMeteorology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.245
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it