A Case Study on Green Areas Change-Detection in Baghdad Using Artificial Intelligence
Bibliographic record
Abstract
As our cities expand and more people migrate into already crowded regions, green areas in cities minimize the effects of pollution and help reduce the urban heat island effect. Adhamiya in Baghdad is one of these urban fabrics that are suffering nowadays from crowded urban fabric with a shortage of green lungs; therefore, in response to these rapid changes and the need to keep an eye on them. This research presented a study based on artificial intelligence, which took advantage of HSV spectrum by restricting it to a group of colors that represent the colors of the green areas, as well as the generation of masks and use of them in the design of the study, as these technologies might provide speedy findings and contribute to the formulation of real-time judgments to examine examples of tissue changes and their influencing elements. Changes in the Green areas of urban fabric were analyzed using artificial intelligence has made considerable progress in exploring and deducing real-time changes and monitoring the environment. The results revealed a drop in the ratio of green areas from 22.45% to 5.46%. This serious indicator necessitates intervention by decision leaders to rectify the situation due to an important correlation between the decline in green areas and the increase in temperature in the region.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".