Estimating Pollution Contents in an Urban Area using Airborne Hyperspectral Thermal Data
Bibliographic record
Abstract
The best remedy of pollution is its detection and control. The major pollutants in an urban area are asbestos and aerosol. This work introduces a technique to detect the pollutants in an area using hyperspectral data. Due to its enriched spectral information very minute contents are identified. The hyperspectral image is captured with the Telops' HyperCam which contains thermal infrared bands which is used to create temperature map of that land surface thereby the different objects are classified according to different temperature ranges. The visible bands are also used to classify the image and the percentage of area under each class is calculated. To assess the accuracy of each classifier, confusion matrix is computed and identified that Support Vector Machine (SVM) classifier is the best other than Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) having accuracy of 94.89%. The amount of aerosol present in a locality is calculated with respect to a factor called PM10which gives the concentration of particles of dimension less than 10 μm. Using the relation between PM10and atmospheric reflectance the value of PM10is obtained between 34 μg/m3and 66 μg/m3. It's level is above 15 μg/m3which is the safe value according to Canadian jurisdiction so there is chance of hazardous health effects on human beings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".