Streetscape augmentation using generative adversarial networks: Insights related to health and wellbeing. (August 2019)

• Record Type: Journal Article
• Authors: Wijnands, Jasper S.; Nice, Kerry A.; Thompson, Jason; Zhao, Haifeng; Stevenson, Mark
• Is Part Of: Sustainable cities and society. Volume 49(2019)
• Publication Date: 2019-08
• Subjects: Generative adversarial network -- Style transfer -- Design -- Street view -- Health -- Wellbeing; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2019.101602
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: This paper illustrates how techniques based on artificial intelligence can objectively analyse large streetscape imagery datasets in relation to health outcomes. Unsupervised feature selection overcomes problems related to selective data collection in previous studies of urban form. By generating new streetscape images through variation of these identified features, the complex relationships between urban form and health are visualised. Besides exploring the perception and impact of streetscape design, our research is a first step towards the production of computer-generated design interventions. Abstract: Deep learning using neural networks has provided advances in image style transfer, merging the content of one image (e.g., a photo) with the style of another (e.g., a painting). Our research shows this concept can be extended to analyse the design of streetscapes in relation to health and wellbeing outcomes. An Australian population health survey ( n = 34, 000) was used to identify the spatial distribution of health and wellbeing outcomes, including general health and social capital. For each outcome, the most and least desirable locations formed two domains. Streetscape design was sampled using around 80, 000 Google Street View images per domain. Generative adversarial networks translated these images from one domain to the other, preserving the main structure of the input image, but transforming the 'style' from locations where self-reported health was bad to locations where it was good. These translations indicate that areas in Melbourne with good general health are characterised by sufficient green space and compactness of the urban environment, whilst streetscape imagery related to high social capital contained more and wider footpaths, fewer fences and more grass. Beyond identifying relationships, the method is a first step towards computer-generated design interventions that have the potential to improve population health and wellbeing.