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 the high input data needs of existing estimation approaches. We introduced a computer vision method to estimate annual means for air pollution levels from street-level images. We used annual mean estimates of NO2 and PM2.5 concentrations from locally calibrated models as labels from London, New York, and Vancouver to allow for compilation of a sufficiently large dataset (~250 k 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 images from the same city (R2 values between 0.51 and 0.95 when validated on data from ground monitoring stations). Similar to LUR models, transferability of models between cities in different geographies is more difficult. Specifically, transferability between the three cities (London, New York, and Vancouver), which have similar pollution source profiles, was moderately successful (R2 values between zero and 0.67). Comparatively, performances when transferring models trained on cities with very different source profiles, such as Accra in Ghana and Hong Kong, were lower (R2 between zero and 0.21). This suggests a need for local calibration, using additional measurement data from cities that share similar source profiles.

Beyond here and now: Evaluating pollution estimation across space and time from street view images with deep learning

“…We outline the different experimental setups for previous studies in Appendix Table A . In the specific case of cities in Africa, one study used street-view images to predict PM 2.5 and NO 2 across several cities, including Accra ( Suel et al, 2022 ). Data used for model training were derived from modelled estimates of annual average pollution level with a model only evaluated, not trained, on data from Accra.…”

Urban Air-Quality Estimation Using Visual Cues and a Deep Convolutional Neural Network in Bengaluru (Bangalore), India

“…However, averaging year-long data does not enable temporal analysis. In other studies, researchers have utilized deep convolutional neural networks (CNNs) to predict AQ directly from images. − These methods all used the black box approach, where the model extracts features from the raw data without physical reasoning.…”