Understanding Satellite Image-Based Green Space Distribution for Setting up Solutions on Effective Urban Environment Management
Bibliographic record
Abstract
Urban environments are vulnerable, as there is a change in the surface structure of the land cover. Particularly when natural vegetation cover is converted to construction land, which is covered by impervious surfaces, the accumulation of solar energy is increased. This has led to an increasingly urban environment that is becoming more severe and threatening to affect the quality of life in urban populations. Satellite images are very helpful in determining the distribution of green space. This paper presents the results of analyzing urban land cover for determining green space (GS) distribution for Ho Chi Minh City (HCMC). In 2017, the vegetation land of the old 13 urban districts accounts for only one third of the impervious surface. In contrast, in the area of six new urban districts there is a high percentage of urban green space, accounting for nearly twice the proportion of the impervious surface type. This shows that the old inner city area is seriously lacking GS area. Most districts have a very low GS index, less than 10 m2/person, while in some districts it is even less than 3 m2/person. In the eastern part of the city, District 9 has the highest GS index, ensuring a good life quality. Recent research has provided a number of management solutions to improve and develop the GS area, thus enhancing the environment quality and the life quality for the population. Moreover, our research results contribute to the effective urban management of HCMC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".