Extended Reality in Spatial Sciences: A Review of Research Challenges and Future Directions
Bibliographic record
Abstract
This manuscript identifies and documents unsolved problems and research challenges in the extended reality (XR) domain (i.e., virtual (VR), augmented (AR), and mixed reality (MR)). The manuscript is structured to include technology, design, and human factor perspectives. The text is visualization/display-focused, that is, other modalities such as audio, haptic, smell, and touch, while important for XR, are beyond the scope of this paper. We further narrow our focus to mainly geospatial research, with necessary deviations to other domains where these technologies are widely researched. The main objective of the study is to provide an overview of broader research challenges and directions in XR, especially in spatial sciences. Aside from the research challenges identified based on a comprehensive literature review, we provide case studies with original results from our own studies in each section as examples to demonstrate the relevance of the challenges in the current research. We believe that this paper will be of relevance to anyone who has scientific interest in extended reality, and/or uses these systems in their research.
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.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".