Optimizing the urban thermal environment at local scale: a case study in Wuhan, China
Bibliographic record
Abstract
The urban thermal environment deteriorates with increasing frequency of extreme heat events in cities. Conventionally, the Urban Heat Island (UHI) effect only reflects the temperature difference between the city and its rural surroundings.This scale of analysis is often too broad to help devise mitigation strategies, which are typically implemented at a more local scale within the sphere of urban planning and design.In this research, the city of Wuhan, China, is taken as an example.Through quantitative measurements, a workflow is proposed to mitigate the surface UHI of Wuhan, locally.Also, the satellite images of the MODerate-resolution Imaging Spectroradiometer and Landsat-7 ETM+ are used for technical purposes, and the K-means clustering is applied to classify the Local Climate Zone (LCZ).Further, the Local Scale Urban Heat Island (LSUHI) is captured through morphological parameters, such as Multi-Scale Shape Index (MSSI) based upon the latent Land Surface Temperature (LST) pattern.The mitigation process is organized hierarchically and prioritized by the combination of LCZ and LSUHI.Based on this, Wuhan is divided into seven LCZs and the LSUHI, in the mean time, can be detected by morphological parameters.We present the corresponding quantitative planning advice for places with higher heat threats in the city.This research is conducted on urban microclimate and urban planning on at least two levels: (1) the reduced study scale of urban thermal environment and (2) a planning-driven workflow of urban thermal environment optimization.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".