3D Geovisualization Interfaces as Flood Risk Management Platforms: Capability, Potential, and Implications for Practice
Bibliographic record
Abstract
Recent advances in technology and workflows related to 3D geovisualization present numerous opportunities for development and evaluation of the usefulness of these tools for analysis and communication of environmental risks. This article explores how cartographic tools currently used for understanding and managing flood risks could be improved through the use of emerging 3D visualization approaches. The topological and dimensional realism enabled by these platforms has the potential both to improve the quality of representation and analysis and to reduce the knowledge barriers impeding understanding of flood risk by nonexpert audiences in risk communication. Furthermore, emerging mixed-reality interfaces offer multiple advantages over desktops for interaction with 3D content. The significant recent growth in both the interface and visualization domains represents an opportunity for researchers and practitioners to evaluate the contributions of these approaches to real-world planning and risk management. In this study, we overview the recent trends in the realm of flood risk visualization and the contributions mixed reality can have for the field. We then present a pragmatic workflow that enables integration of rigorous geospatial data related to flooding into a 3D visualization environment, to illustrate how various interface platforms can easily be integrated and evaluated.
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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 | 0.042 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".