High-Precision Intracity Temperature Estimation Based on Generated Point Clouds
Bibliographic record
Abstract
Estimation of urban surface temperature is crucial for urban planning and emergency management. Due to the complexity of intracity structures, it is very challenging to acquire satisfied prediction errors of the land surface temperature (LST) at very high resolution, like 60-by-60 m. Considering this, we propose a low-cost method for generating urban point clouds via readily accessible city data. Then we design an efficient descriptor, geofeature distribution matrix (GFDM) to describe the complex intracity structure. Using GFDM, we introduce a 3-D urban structure guided temperature prediction network (3D-UP Net) to capture the complex relationship between urban structure, upper atmospheric conditions, and surface temperature. The proposed 3D-UP Net is generalizable, capable of predicting future surface temperature for existing cities and even for those that are planned. Experiments conducted in multiple regions of China demonstrate that our method’s error is less than 1.5 K (in most cases) at a high resolution (60-by-60 m).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".