Evaluation of urban heat island effects based on fine-resolution mapping of subpixel impervious surface dynamics over four cities in China
Bibliographic record
Abstract
• We propose UISP for subpixel impervious surface extraction using Landsat imagery • UISP reveals urban sprawl and enhances SUHI analysis beyond pixel-scale limits • High ISP values are strongly associated with intensified urban heat island effects • UISP supports refined urban heat management and resilient city development policies In the context of global urbanization, urban expansion significantly affects urban thermal environment. As a key indicator of urbanization, impervious surfaces are widely used to monitor urban growth. However, most existing studies examining thermal environments through impervious surfaces are limited to pixel-scale. Given the complexity and heterogeneity of urban land cover, pixel-scale analyses often fail to accurately capture the thermal effects of impervious surfaces. To address this issue, we propose a universal impervious surface percentage (UISP) model based on Landsat imagery for subpixel-scale impervious surfaces monitoring. We evaluated the relationship between impervious surface percentage (ISP) and surface urban heat islands (SUHI) in four representative Chinese cities—Beijing, Kunming, Shenzhen, and Wuhan—from 2018 to 2023. Results indicate that UISP significantly improves the accuracy of impervious surface estimation over heterogeneous urban landscapes. The deviations of estimated ISP are as much as 0.2 lower than pixel-scale results. In SUHI areas, ISP values exceeded 0.4 to 0.6 higher than in non-SUHI regions. The ISP difference between SUHI levels 1 and 2 was 0.12, and between levels 5 and 6, it was 0.08, suggesting ISP saturation in high SUHI zones. This study introduced UISP for subpixel extraction of impervious surfaces and thermal environment analysis, offering a novel approach for precise quantification of urbanization and insights into thermal environment research.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".