Interactive 3D Visualization and Dissemination of UAV-SfM Models for Virtual Outcrop Geology
Bibliographic record
Abstract
The potential of high resolution 3D datasets is being increasingly realized in an expanding number of geoscience applications. However, sharing of these datasets and interpretations requires end-users to have specialty software programs and high-end processing computers. Although user-friendly technological advances, such as uninhabited aerial vehicles (UAVs or drones) and structure-from-motion (SfM) photogrammetry, provide geoscientists with tools to easily collect, process, and analyze 3D models at multiple scales, dissemination of results to the general public commonly revert to conventional 2D formats, such as (static) 2D maps, figures, and rigid animations/videos. To facilitate dissemination of complete 3D datasets and interpretations to a wider audience, we review three modern platforms that enable visualization, sharing, and publishing of various 3D formats. We demonstrate the capabilities and limitations of each visualization platform by presenting a 3D digital outcrop model (DOM) of an extensive exposure of fluvial channel belt deposits in a 1 km2 area of Dinosaur Provincial Park (Alberta, Canada) generated from UAV-SfM photogrammetry. Each visualization platform provides intuitive controls and accessibility on standard desktop computers through web-based browsers (e.g., Sketchfab and potree) or a standalone executable file developed through videogame engines, such as Unity or Unreal Engine. Proprietary viewers allow straightforward sharing of 3D models, but limit size, detail, and resolution and also have restricted means for accommodating interpretations. Open-source platforms afford more functionality, facilitate additional 3D datasets, and can provide customized visualization experiences for end-users, but may require more advanced coding experience. Visualization platforms examined within this study offer access to large 3D datasets without the need for specialized software and advanced computing hardware. Further development and use of such platforms has potential to enhance student education and improve scientific communication through unique customizable experiences that allow for democratization of high resolution 3D datasets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".