Mixed Reality Flood Visualizations: Reflections on Development and Usability of Current Systems
Bibliographic record
Abstract
Interest in and use of 3D visualizations for analysis and communication of flooding risks has been increasing. At the same time, an ecosystem of 3D user interfaces has also been emerging. Together, they offer exciting potential opportunities for flood visualization. In order to understand how we turn potential into real value, we need to develop better understandings of technical workflows, capabilities of the resulting systems, their usability, and implications for practice. Starting with existing geospatial datasets, we develop single user and collaborative visualization prototypes that leverage capabilities of the state-of-the art HoloLens 2 mixed reality system. By using the 3D displays, positional tracking, spatial mapping, and hand- and eye-tracking, we seek to unpack the capabilities of these tools for meaningful spatial data practice. We reflect on the user experience, hardware performance, and usability of these tools and discuss the implications of these technologies for flood risk management, and broader spatial planning practice.
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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 | 0.075 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".