Tobacco marketing, restricted almost exclusively to the point-of-sale in recent years, has proven to be effective in getting more people to consume and fewer to quit cigarettes and smokeless tobacco products. The lack of empirical documentation linking product exposure to behavior, however, is a key obstacle to the adoption of additional restrictions on point-of-sale tobacco advertising. The goal of this project is to map point-of-sale tobacco marketing practices across New York City using automated detection of tobacco signage in street-level imaging data. Convolutional neural networks, which are particularly effective at detecting objects in images, were trained to identify and classify outdoor advertisements of cigarettes and smokeless tobacco.  Previous analyses of visual data in public health research involving manual image coding are prohibitively costly and time-consuming. The importance and motivation of the project stems from the immediate and comprehensive effect of tobacco advertisements on its sales and consequently on public health.  Detected advertisements derived from our model output provide a proof-of-concept for measuring exposure of at-risk communities to tobacco displays.