An Innovative Low-Altitude Dual-Source Remote Sensing Platform for Urban Thermal Environment Observation: Integration, Tests, Optimization, and Assessment
Bibliographic record
Abstract
The urban thermal environment significantly influences regional climate change, urban resilience, and sustainability. Although traditional remote sensing technologies are common tools for urban thermal environments observation, it has limitations including low spatial resolution, long revisit cycles, and cloud cover. In responding to these limitations, a low-altitude dual-source remote sensing platform (LADSRSP) was researched and developed in this study through a series of tests. First, a thermal infrared sensor and a hyperspectral sensor were simultaneously equipped on a multi-rotor drone via a customized three-axis gimbal in LADSRSP. Furthermore, LADSRSP was optimized based on the stability test results, and was then confirmed to have the ability of collecting data with high spatial resolution and quality through the data acquisition tests. Last, the performance of LADSRSP was assessed by data integrity, classification accuracy, and temperature analysis. The image classification results demonstrate high accuracy with an overall classification accuracy of 97.92% and a Kappa coefficient of 0.97 from collected data quality analysis tests. The temperature values of each urban underlying surface were then accurately extracted to facilitate further statistical analysis. Overall, the LADSRSP was proved to be a feasible, efficient, and accurate tool for urban thermal environment research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".