Palmer, Gregory, Green, Mark ORCID: 0000-0002-0942-6628, Boyland, Emma ORCID: 0000-0001-8384-4994, Vasconcelos, Yales Stefano Rios, Savani, Rahul ORCID: 0000-0003-1262-7831 and Singleton, Alex
(2021)
A deep learning approach to identify unhealthy advertisements in street view images.
SCIENTIFIC REPORTS, 11 (1).
4884-.
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Abstract
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, which may encourage 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 [Formula: see text] 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). 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.
Item Type: | Article |
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Additional Information: | 13 pages, 5 figures, 3 table. To appear in Nature Scientific Reports |
Uncontrolled Keywords: | cs.CY, cs.CY, cs.LG |
Depositing User: | Symplectic Admin |
Date Deposited: | 09 Feb 2021 08:11 |
Last Modified: | 18 Jan 2023 23:00 |
DOI: | 10.1038/s41598-021-84572-4 |
Open Access URL: | https://doi.org/10.1038/s41598-021-84572-4 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3115344 |
Available Versions of this Item
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A deep learning approach to identify unhealthy advertisements in street view images. (deposited 28 Sep 2020 08:04)
- A deep learning approach to identify unhealthy advertisements in street view images. (deposited 09 Feb 2021 08:11) [Currently Displayed]