Using Mixed Reality for the Visualization and Dissemination of Complex 3D Models in Geosciences—Application to the Montserrat Massif (Spain)
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.
Bibliographic record
Abstract
In the last two decades, both the amount and quality of geoinformation in the geosciences field have improved substantially due to the increasingly more widespread use of techniques such as Laser Scanning (LiDAR), digital photogrammetry, unmanned aerial vehicles, geophysical reconnaissance (seismic, electrical, geomagnetic), and ground-penetrating radar (GPR), among others. Furthermore, the advances in computing, storage and visualization resources allow the acquisition of 3D terrain models (surface and underground) with unprecedented ease and versatility. However, despite these scientific and technical developments, it is still a common practice to simplify the 3D data in 2D static images, losing part of its communicative potential. The objective of this paper is to demonstrate the possibilities of extended reality (XR) for communication and sharing of 3D geoinformation in the field of geosciences. A brief review of the different variants within XR is followed by the presentation of the design and functionalities of headset-type mixed reality (MR) devices, which allow the 3D models to be investigated collaboratively by several users in the office environment. The specific focus is on the functionalities of Microsoft’s HoloLens 2 untethered holographic head mounted display (HMD), and the ADA Platform App by Clirio, which is used to manage model viewing with the HMD. We demonstrate the capabilities of MR for the visualization and dissemination of complex 3D information in geosciences in data rich and self-directed immersive environment, through selected 3D models (most of them of the Montserrat massif). Finally, we highlight the educational possibilities of MR technology. Today MR has an incipient and reduced use; we hope that it will gain popularity as the barriers of entry become lower.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it