Spatial distribution of urban vegetation: A case study of a Canadian University Campus using LiDAR-based metrics
Bibliographic record
Abstract
Planners and urban managers design green spaces according to established standards, aspiring to create green spaces within and around the built environment. However, when building density is extremely high, it is difficult to design large, accessible green spaces. Urban green spaces are even more necessary when built density increases, and it is important to maintain urban vegetation—especially trees—as a major and integral part of the cities. Therefore, examining the distribution of urban vegetation is a tool for policymakers and community groups seeking to simultaneously moderate urban heat-island effects, and mitigate the effects of greenhouse gas emissions. The purpose of this study was to compare three different urban vegetation indices in a university campus for quantifying spatial relationships between green and gray infrastructure. Light Detection and Ranging (LiDAR) data were used to assess the distribution of urban vegetation. The indices varied significantly among various buildings according to their use categories (e.g., academic, administrative, etc.). These differences could be used to estimate the provision of ecosystem services for the various use categories and to evaluate trade-offs. For example, higher tree densities should provide greater rates of carbon sequestration and storage, as well as water retention and flood mitigation. Conversely, aesthetic and security considerations might favor lower vegetation density to preserve sight lines and vistas. The tools employed in this study have potential for use at greater scales, including entire cities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".