Data Collection in Svalbard, Norway to Test the use of Virtual Reality for Lunar and Planetary Surface Exploration
Bibliographic record
Abstract
As part of MIT's work with the Resource Exploration and Science of our Cosmic Environment (RESOURCE) project with NASA Ames and the Solar System Exploration Research Virtual Institute we are testing both the scientific and operational usefulness of a virtual reality environment for local, small-scale (< 5 m) geological analysis for Lunar and planetary surface exploration missions. Specifically, we are testing a virtual reality (VR) environment developed using a low-cost commercial off-the-shelf combination LiDAR/RGB camera for geological exploration. We incorporate local environmental data such as temperature, luminosity, humidity, and wind. The data was collected in Svalbard, Norway, from three locations near Longyearbyen. The sites were selected based on their distinct geological features including 1) a riverbed in a glacially carved valley (<10 cm-scale features), 2) the base of a recent glacial retreat (last 100 years, 10-50 cm) and 3) a permafrost feature (>1 m). This data is rendered in VR and will be used to assess scientist's abilities to answer questions about the relevant local geology for differing feature scales (<10 cm, 10-50 cm and >1 m). The VR environment will be compared to a traditional desktop application in 2-dimensions and geological field notes taken on an app developed by the University of Western Ontario for geological field work. Here we discuss the data collection techniques used in Svalbard as well as the lessons learned from this field work. Data processing techniques are presented, as well as the final VR rendering capabilities developed for this work. The low cost of the technologies provides an opportunity to develop immersive environments not only for Lunar and planetary surface exploration, but also for remote or environmentally sensitive locations on Earth where research is lacking and human presence should be minimized.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".