A novel reference station-based methodology for high-resolution urban temperature mapping utilizing machine learning techniques
Bibliographic record
Abstract
The formation of Urban Heat Islands (UHI) creates substantial effects on building energy usage alongside human comfort standards throughout the world's urban areas. Current methods for mapping urban temperatures struggle to create a balance between detailed spatial coverage and accurate time-specific data. In this research, we designed a reference station-based method to create high-resolution temperature maps of urban areas at low cost, which is implemented in Shenzhen, China as the case study. A combination of Local Climate Zone classification with satellite data and machine learning algorithms generates spatiotemporally continuous temperature field results. The XGBoost-based mapping framework can achieve an MAE of 0.56°C with an R² value of 0.980. Building simulation together with thermal comfort analysis can benefit substantially from this methodology as it allows users to develop representative high-resolution microclimates through Typical Meteorological Year (TMY) weather data. The created model enables architects and engineers and urban planners to support their decisions in building design, climate change adaptation, and energy management practices. The developed approach delivers advanced air temperature mapping at affordable costs and requires easy implementation. The proposed data collection method offers detailed temperature information with high spatial resolution and temporal precision which makes it possible to improve urban planning and forecast building as well as renewable energy system performance in urban areas.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".