What you see is what you breathe? Estimating air pollution spatial variation using street level imagery.

ABSTRACT: High spatial resolution information on urban air pollution levels is unavailable in many areas globally, partially due to high input data needs of existing estimation approaches. Here we introduce a computer vision method to estimate annual means for air pollution levels from street level images. We used annual mean estimates of NO₂ and PM_2.5 concentrations from locally calibrated models as labels from London, New York, and Vancouver to allow for compilation of a sufficiently large dataset (~250k images for each city). Our experimental setup is designed to quantify intra and intercity transferability of image-based model estimates. Performances were high and comparable to traditional land-use regression (LUR) and dispersion models when training and testing on images f

SUBMITTER: Suel E 

PROVIDER: S-EPMC7615101 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature