Predicting air pollution spatial variation with street-level imagery
Bibliographic record
Abstract
Air pollution has important and widely accepted implications on health outcomes [1,2,3,4].Advances in spatial and temporal estimation have been instrumental both in establishing health effects [2] and estimating impact on the burden of disease [1,5].Yet, we currently lack information on air pollution levels in many areas globally [1], especially at high spatial resolution.Limited spatial and temporal coverage of air quality models is partially due to input data requirements of existing estimation approaches including physically-based e.g., dispersion models, and geostatistical e.g., land use regression (LUR) models [6].Advances in deep learning methods and their success in computer vision applications led to a growing interest in using images for estimating air pollution levels [6,7,8,9,10,11,12,13].The rationale behind this interest is that information on common inputs to traditional approaches (e.g.land use, traffic, and built environment features) is, at least partially, visible from street-level and satellite images.If utilized effectively, this approach has the potential for scaling up to global coverage at high spatial and temporal resolution with increasing availability of imagery at low cost.Building on existing work, for this study, we obtained LUR based mean annual NO 2 (200m resolution raster) and PM 2.5 (100m resolution raster) estimates for 2010 from four cities: London, New York, Los Angeles, and Vancouver (BC) [14,15,16,17,18].To obtain images, a 100m grid for each city was created using the boundary shape files covering LUR model outputs.For each of the grid centroid points, we obtained the nearest street-level panorama image available from Google Street View that
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".