Evaluating Urban Heat Island Effects in Malang City Parks Using UAV and OBIA Technologies
Bibliographic record
Abstract
This study aims to evaluate the effectiveness of Object-Based Image Analysis (OBIA) in classifying high-resolution satellite and drone imagery, and its application in mapping the distribution of Land Surface Temperature (LST) in Malang City parks.Malang was chosen because of its unique urban dynamics and the challenges faced in managing the urban heat island effect.Using data from thermal UAVs, this study provides a detailed analysis of urban microscale green spaces.OBIA is applied to classify areas based on surface material and vegetation, resulting in an accurate mapping of LST variations.The results show that areas with dense vegetation such as Merdeka Square Park have lower LST values, while areas with hard surfaces such as asphalt and concrete show higher LST values.This study reveals that 22.13% of the area has very low temperatures (31-36℃) and 30.05% with high temperatures (46-51℃).The implications of these results are very relevant for policy development, showing that increasing urban green space, planting wide canopy trees, using environmentally friendly materials, and adding water elements have a significant impact on reducing the urban heat island effect and increasing thermal comfort.These recommendations can be integrated into the urban planning strategy of Malang and other cities with similar characteristics to support the sustainability of the urban environment globally.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".