  While outdoor advertisements are common features within towns and cities,
they may reinforce social inequalities in health. Vulnerable populations in
deprived areas may have greater exposure to fast food, gambling and alcohol
advertisements encouraging their consumption. Understanding who is exposed and
evaluating potential policy restrictions requires a substantial manual data
collection effort. To address this problem we develop a deep learning workflow
to automatically extract and classify unhealthy advertisements from
street-level images. We introduce the Liverpool 360 Street View (LIV360SV)
dataset for evaluating our workflow. The dataset contains 25,349, 360 degree,
street-level images collected via cycling with a GoPro Fusion camera, recorded
Jan 14th - 18th 2020. 10,106 advertisements were identified and classified as
food (1335), alcohol (217), gambling (149) and other (8405) (e.g., cars and
broadband). We find evidence of social inequalities with a larger proportion of
food advertisements located within deprived areas and those frequented by
students. Our project presents a novel implementation for the incidental
classification of street view images for identifying unhealthy advertisements,
providing a means through which to identify areas that can benefit from tougher
advertisement restriction policies for tackling social inequalities.
