A Deep Learning Approach for Meter-Scale Air Quality Estimation in Urban Environments Using Very High-Spatial-Resolution Satellite Imagery

Abstract

High-spatial-resolution air quality (AQ) mapping is important for identifying pollution sources to facilitate local action. Some of the most populated cities in the world are not equipped with the infrastructure required to monitor AQ levels on the ground and must rely on other sources, such as satellite derived estimates, to monitor AQ. Current satellite-data-based models provide AQ mapping on a kilometer scale at best. In this study, we focus on producing hundred-meter-scale AQ maps for urban environments in developed cities. We examined the feasibility of an image-based object-detection analysis approach using very high-spatial-resolution (2.5 m) commercial satellite imagery. We fed the satellite imagery to a deep neural network (DNN) to learn the association between visual urban features and air pollutants. The developed model, which solely uses satellite imagery, was tested and evaluated using both ground monitoring observations and land-use regression modeled PM2.5 and NO2 concentrations over London, Vancouver (BC), Los Angeles, and New York City. The results demonstrate a low error with a total RMSE < 2 µg/m3 and highlight the contribution of specific urban features, such as green areas and roads, to continuous hundred-meter-scale AQ estimations. This approach offers promise for scaling to global applications in developed and developing urban environments. Further analysis on domain transferability will enable application of a parsimonious model based merely on satellite images to create hundred-meter-scale AQ maps in developing cities, where current and historical ground data are limited.

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

“…Two studies, also from Accra, recorded PM 2.5 and PM 10 in selected neighbourhoods, in a multi-week measurement campaign ( Dionisio et al, 2010 ; Rooney et al, 2012 ), together with researcher observations and census data on environmental factors, such as biomass fuels and unpaved roads, to predict pollution levels. Some studies have also predicted pollution using remote sensing data, which differs from our study, not only in the view of the city, but also in spatial and temporal scales and the observable features in images ( Sorek-Hamer et al, 2022 ; Wei et al, 2020 ; Weigand et al, 2019 ).…”