ArticleScientific reports2021
A deep learning approach to identify unhealthy advertisements in street view images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Building Graduate Capabilities Through a Course-Wide Public Health and Health Promotion ECHA AI Curriculum Framework.Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals · 2026Article
- Evaluating AI models for food and alcohol advertisement classification against human benchmarks.Scientific reports · 2026Article
- Street-level imagery dataset for the detection of informal vendors in urban environment.Data in brief · 2025Article
- Mapping Advertising Assets Project: a cross-sectional analysis of food-related outdoor advertising and the relationship with deprivation in Leeds, UK.Public health nutrition · 2025Article
- Revolutionising food advertising monitoring: a machine learning-based method for automated classification of food videos.Public health nutrition · 2023Article
- Towards effective restriction of unhealthy food marketing to children: unlocking the potential of artificial intelligence.The international journal of behavioral nutrition and physical activity · 2023Article
- A scoping review of outdoor food marketing: exposure, power and impacts on eating behaviour and health.BMC public health · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.