Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Streetscape augmentation using generative adversarial networks: insights
  related to health and wellbeing   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.

27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc