MODELLING LAND-USE REGULATION CONFLICTS WITH 3D COMPONENTS TO SUPPORT ISSUING A BUILDING PERMIT
Bibliographic record
Abstract
Abstract. Cities are facing important challenges due to population growth and massive development of high-rises and complex structures above and below the ground surface. In that respect, having an efficient land-use regulation framework in force is necessary for cities. In investigating current practices for processing spatial data when issuing building permits, in many cases, the planned building is drawn on 2D plans with cross-sections to represent their 3D dimensions. In complex multilevel developments, this method has significant shortcomings like the requirement of managing numerous plans and sections, and uncertainty in decisions more specifically when checking land-use regulations comprising 3D components (e.g. height limits, overhanging objects, solar rights). In order to support issuing a building permit and moving towards the establishment of 3D smart cities, this paper presents an inventory for land-use regulations with 3D components and functional classification of their possible conflicts. Two functional classifications of possible conflicts in a building permit process from two points of view (i.e. data integration process, and magnitude of land-use regulation conflicts) are proposed. These results are placed in the context of having 3D city models that integrate land-use regulation information.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".