Many epidemiological studies examining the health effects of air pollution have measures of air pollutant concentrations collected from sparse networks of stationary ground monitors as their main exposure metric. Continuous, high-spatial-resolution air AQ estimates will help with source identification, facilitate local awareness, improve the accuracy and specificity of health effects studies, and be useful for tracking impacts of air quality management. Over the past several decades, different models have been used to estimate air quality, with improved spatial coverage and reduced bias. These include dispersion modeling [8], regression kriging [9], land-use regression (LUR) [10; 11; 12; 13; 14], and satellite-based models [15; 16; 17]. The satellite-based models use retrieved variables such as aerosol optical depth (AOD) as explanatory variables. Satellite-based estimates are increasingly being used to determine continuous exposure metrics in health studies at coarse resolutions (1 km-10 km) and with limitations related to column-surface calibration. Assessments of long-term air pollution exposure in environmental health studies have commonly employed LUR or chemical transport modeling (CTM) techniques. Whereas CTM requires local emissions data, LUR is a commonly used algorithm for urban regions that require substantial local (measured) data such as traffic, meteorology, and spatially disaggregated population data. LUR requires gathering many datasets on a specific location and therefore is usually performed on an annual basis [11; 13]. Due to large input data requirements of LUR models, coverage cannot easily be scaled up. As a result, we currently lack high-spatial-resolution estimates of pollutant levels in many cities around the world, especially in low- and middle-income countries. LUR modeling uses multiple regression equations to describe the relationship between sample locations and environmental variables. Resulting models can predict pollution concentrations at unmeasured locations, usually with relatively high spatial resolution. In addition, satellite-based retrieved data (e.g., AOD, temperature) have been used for estimating concentrations of air pollutants mostly combined with additional explanatory variables [18; 19; 20; 21]. The great allure of satellite data is their global coverage. These models perform well where historical ground monitoring data and ancillary data are available, and calibration is possible, which facilitates the production of continuous air pollutant concentration maps at a relatively coarse spatial resolution (i.e., on the kilometer scale). The spatial resolution of available satellite imagery has increased significantly due to the existence of commercial satellites that obtain daily images of the globe at a meter-scale resolution. Current developments in applying deep learning methods to satellite imagery, and not only to satellite-borne retrieved variables as AOD, have recently been used to improve capabilities of estimating air quality [22; 23] and socioeconomic and environmental factors [24; 25; 26]. Visual features in high-spatial-resolution satellite imagery contain significant information relevant to urban air quality. This will allow the next generation of AQ models to estimate hundred-meter-scale air quality concentrations in urban environments, potentially without requiring any locally collected ground data. This study demonstrates a novel deep learning approach based on very high-spatial-resolution satellite imagery (2.5 m) to estimate PM\({}_{2.5}\) and NO\({}_{2}\) annual mean concentrations in urban environments. The analysis was conducted on Greater London, UK; Vancouver, Canada; and Los Angeles (LA), USA and evaluated on an unseen city--New York City (NYC), USA in 2010 (datasets and methodology are detailed in the 'Data and Methods' section and in the Supplementary Material). Results from the multi-location model are validated with ground measurements and with 2010 LUR models (which serve as our target data).