High-resolution local climate zone mapping via deep mixed-scene decomposition of remote sensing imagery
Bibliographic record
Abstract
Under rapid urbanization, traditional single-class LCZ mapping methods fail to represent the coexistence of multiple land-cover types within the same block, resulting in blurred boundaries and reduced accuracy for urban heat-island modeling. To address this, LCZ mapping is reformulated as a mixed-scene unmixing task and tackled with a novel deep-learning framework, MSU-Net. Real street-block morphologies from OpenStreetMap are combined with 1 m Google Earth imagery to create multi-scale inputs that preserve both global block layouts and local detail. MSU-Net comprises a primary unmixing branch reinforced by two auxiliary guidance branches—one driven by purified-image semantic cues, the other by sparse local spatial reconstruction, and a Dual Cross-Attention Fusion (DCAF) module that integrates global–local and global–purified features under non-negativity and sum-to-one constraints. Two block-level datasets covering Wuhan and Shenzhen were created. MSU-Net outperforms existing methods on these datasets, boosting overall accuracy by 15–17 %, reducing mean absolute error by over 35 %, and cutting weighted-difference error by around 30 %. Transfer learning further confirms its robustness across cities with distinct morphologies.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".