How do People Perceive the City's Green Space? A View from Satellite Imagery (In Hanoi, Vietnam)
Bibliographic record
Abstract
In urban design and management green space is one of the most important components concerning the satisfaction degree of the urban inhabitants. In such a circumstance, it is crucial for land managers and urbanites to know which type of green space the people living in cities prefer. However, this question has not been studied in developing countries in Asia. In this paper we verify the relation between the vegetation density of different green space types (extracted from a satellite image) and people's satisfaction in Hanoi city, Viet Nam. We conducted Quickbird satellite image segmentations and classifications by using image color, geometric and contextual information in the software Definiens 5.0. We then calculated the vegetation density based on administrative units. To evaluate the correlation between the mapped vegetation densities with inhabitants' satisfaction we carried out Spearman statistical tests. With a precision of 79% of image classification, we obtained four types of green space: agriculture, park trees, street-side trees and isolated trees. The results show that people are the mostly satisfied with the green space where street-side trees are the most popular green type.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".