Virtual Reality in Cartography: Immersive 3D Visualization of the Arctic Clyde Inlet (Canada) Using Digital Elevation Models and Bathymetric Data
Bibliographic record
Abstract
Due to rapid technological development, virtual reality (VR) is becoming an accessible and important tool for many applications in science, industry, and economy. Being immersed in a 3D environment offers numerous advantages especially for the presentation of geographical data that is usually depicted in 2D maps or pseudo 3D models on the monitor screen. This study investigated advantages, limitations, and possible applications for immersive and intuitive 3D terrain visualizations in VR. Additionally, in view of ever-increasing data volumes, this study developed a workflow to present large scale terrain datasets in VR for current mid-end computers. The developed immersive VR application depicts the Arctic fjord Clyde Inlet in its 160 km × 80 km dimensions at 5 m spatial resolution. Techniques, such as level of detail algorithms, tiling, and level streaming, were applied to run the more than one gigabyte large dataset at an acceptable frame rate. The immersive VR application offered the possibility to explore the terrain with or without water surface by various modes of locomotion. Terrain textures could also be altered and measurements conducted to receive necessary information for further terrain analysis. The potential of VR was assessed in a user survey of persons from six different professions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".