GEOSPATIAL MODELLING APPROACH FOR 3D URBAN DENSIFICATION DEVELOPMENTS
Bibliographic record
Abstract
With growing populations, economic pressures, and the need for sustainable practices, many urban regions are rapidly densifying developments in the vertical built dimension with mid- and high-rise buildings. The location of these buildings can be projected based on key factors that are attractive to urban planners, developers, and potential buyers. Current research in this area includes various modelling approaches, such as cellular automata and agent-based modelling, but the results are mostly linked to raster grids as the smallest spatial units that operate in two spatial dimensions. Therefore, the objective of this research is to develop a geospatial model that operates on irregular spatial tessellations to model mid- and high-rise buildings in three spatial dimensions (3D). The proposed model is based on the integration of GIS, fuzzy multi-criteria evaluation (MCE), and 3D GIS-based procedural modelling. Part of the City of Surrey, within the Metro Vancouver Region, Canada, has been used to present the simulations of the generated 3D building objects. The proposed 3D modelling approach was developed using ESRI’s CityEngine software and the Computer Generated Architecture (CGA) language.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".