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Automated Image Sampling and Classification Can Be Used to Explore Perceived Naturalness of Urban Spaces

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  • Roger Hyam

Abstract

The psychological restorative effects of exposure to nature are well established and extend to just viewing of images of nature. A previous study has shown that Perceived Naturalness (PN) of images correlates with their restorative value. This study tests whether it is possible to detect degree of PN of images using an image classifier. It takes images that have been scored by humans for PN (including a subset that have been assessed for restorative value) and passes them through the Google Vision API image classification service. The resulting labels are assigned to broad semantic classes to create a Calculated Semantic Naturalness (CSN) metric for each image. It was found that CSN correlates with PN. CSN was then calculated for a geospatial sampling of Google Street View images across the city of Edinburgh. CSN was found to correlate with PN in this sample also indicating the technique may be useful in large scale studies. Because CSN correlates with PN which correlates with restorativeness it is suggested that CSN or a similar measure may be useful in automatically detecting restorative images and locations. In an exploratory aside CSN was not found to correlate with an indicator of socioeconomic deprivation.

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  • Roger Hyam, 2017. "Automated Image Sampling and Classification Can Be Used to Explore Perceived Naturalness of Urban Spaces," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-10, January.
    • Handle: RePEc:plo:pone00:0169357
    DOI: 10.1371/journal.pone.0169357
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    Cited by:

    1. Gabriel Valerio-Ureña & Richard Rogers, 2019. "Characteristics of the Digital Content about Energy-Saving in Different Countries around the World," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
    2. Rietveld, Robert & van Dolen, Willemijn & Mazloom, Masoud & Worring, Marcel, 2020. "What You Feel, Is What You Like Influence of Message Appeals on Customer Engagement on Instagram," Journal of Interactive Marketing, Elsevier, vol. 49(C), pages 20-53.
    3. Gosal, Arjan S. & Geijzendorffer, Ilse R. & Václavík, Tomáš & Poulin, Brigitte & Ziv, Guy, 2019. "Using social media, machine learning and natural language processing to map multiple recreational beneficiaries," Ecosystem Services, Elsevier, vol. 38(C), pages 1-1.

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