DEEPURBANMODELLER (DUM): A PROCESS-INFORMED NEURAL ARCHITECTURE FOR HIGH-PRECISION URBAN SURFACE TEMPERATURE PREDICTION
Bibliographic record
Abstract
Abstract. High-resoulution downscaling of surface climate metrics like urban surface temperature, is a crucial and ongoing research challenge in urban climatology and environmental studies. In this study we propose a groundbreaking Physics-Inspired Neural Architecture for Modeling (PINAM) called DeepUrbanModeller(DUM), designed specifically for urban microclimate temperature estimation. DeepUrbanModeller(DUM) harnesses process-based modelling and satellite remote sensing, and draws upon high-accuracy 3D point clouds to deliver precise estimations of urban Land Surface Temperature (LST) at ultra-high resolutions. By incorporating high-accuracy land surface geometric data sourced from 3D point clouds and guided by the principles of atmospheric physics linked to surface temperature, DeepUrbanModeller(DUM) creates a data-driven framework, informed by physical laws, to accurately model high-resolution temperature distributions a task challenging for numerical simulations or conventional machine learning. The DeepUrbanModeller(DUM) design integrates two key components: Global Physical Feature Interpretation (GPFI) and Local Urban Surface Insight (LUSI). The GPFI captures broader urban physical parameters, ensuring the estimates comply with relevant physical laws. The LUSI enhances estimation performance at high-resolution levels by utilizing a newly proposed Urban Detail Orientation Index (UDOI) derived from 3D point clouds. Experimental results demonstrate the DeepUrbanModeller(DUM)’s superior capability in estimating urban LST on a detailed 30-by-30 meter grid, achieving an estimation error of less than 0.2 Kelvin compared to satellite measurements, a performance surpassing traditional methodologies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".