Regional and urban heat island studies in megacities: A systematic analysis of research methodology
Bibliographic record
Abstract
The paper provides a systematic review of satellite-based regional and urban heat island (RHI and UHI) studies in cities and their challenges, from 2010 to the present based on visualizing scientific landscapes (VOS) viewer analysis and Scopus and science database search using a set of standard criteria. The review results show that 52.17% of the studies used Landsat images followed by MODIS (36.65%). Based on VOS viewer analysis author keywords, remote sensing was strongly linked to urban heat island, urban greenspace, and improvise surface, respectively. Regarding, Co-authorship network China, Canada and the United kingdom’s authors actively collaborated with different world researchers. The most frequently studied regions and periods of research are China and summer daytime, respectively. A total of 55% of the articles reported the use of a mono-window algorithm for retrieving LST from sensors. On the other hand, remotely sensed UHI studies have been facing a series of challenges, including differences between remote sensing satellite-derived LST and air temperature, impacts of clouds and other factors on LST data, methods to quantify UHI, accuracy assessment and attribution of RHI and UHI. Thus, consideration was given to the understudied cities, the methods to compute RHI and/or UHI intensity, inter-annual variability and modeling in the future.
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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 | 0.042 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.043 | 0.039 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".