Development of a collaborative geospatial augmented reality system in support of urban design practice
Bibliographic record
Abstract
Efficient collaboration between participants is fundamental for urban planning and design processes, but it is not necessarily easy to achieve. This limitation depends on several factors including differences in participants' backgrounds and the limitations and the complexity of the tools used to support those process. Furthermore, the collaborative component of urban planning and design projects is often limited to meetings where designs and plans are assembled and discussed. The solution presented in this paper, aims at overcoming those problems through a collaborative geospatial augmented reality application. The proposed system integrates innovative technologies of augmented reality with 3D modelling and spatial analysis tools. Results of our experimentations demonstrate the potential of such a system for improving interactive and collaborative urban design. This paper provides a comparison of our approach with existing ones and proposes future developments in the field. RESUME. Si la bonne collaboration entre participants est fondamentale dans les processus d'amenagement et de design, elle n'est pas toujours facile a mettre en œuvre. Differents facteurs expliquent cette difficulte, allant des differences d'origines des participants, a la complexite des outils utilises par les experts. Par ailleurs, la composante collaborative des projets d'amenagement et de design est souvent limitee aux seules phases d'assemblage et de discussions sur les plans. Precisement, dans cet article nous proposons une solution fondee sur une application de realite augmentee geospatiale collaborative. Le systeme propose associe des technologies de realite augmentee innovantes et des outils de modelisation 3D et d'analyse spatiale. Les resultats de nos tests montrent la pertinence de ce type de systeme pour ameliorer l'interaction et la collaboration en design urbain. Cet article fournit une comparaison entre l'approche developpee et les approches existantes et propose finalement des voies de developpement futures.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".