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Journal articles

Coupling crowd-sourced imagery and visibility modelling to identify landscape preferences at the panorama level

Abstract : Geotagged photos posted on photo-sharing platforms have recently become a new source of information for analysing landscape preferences and investigating the aesthetic dimension of cultural ecosystem services. Most studies seek to explain photo density by landscape or spatial characteristics that might account for individual preferences and aesthetic criteria favoured by photographers. We focus instead on a “panorama level” of analysis, based on the assumption that photos represent preferential directions within a given panorama. The analysis consists in comparing the content of the photographed views with the content of the antipodal views (i.e. the view at 180°). We apply this method to a set of Flickr photos taken in the Lake Geneva region (Switzerland and France) characterised by landscape descriptors based on a visibility modelling approach. The results of discrete choice modelling at the global level are consistent with several key concepts of landscape preferences (e.g., openness, naturalness). The local analyses conducted at eight photo hotspots confirm the influence of open landscapes while revealing variations for certain other landscape characters depending on the geographical setting. We conclude that the panorama level approach combining geotagged photos and visibility modelling is suitable for identifying the landscape signature of the most appealing views. This signature could be used in further studies to detect the potential of visual amenities.
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Contributor : Théoriser et modéliser pour aménager (UMR 6049) Université de Bourgogne Franche-Comté Connect in order to contact the contributor
Submitted on : Thursday, March 5, 2020 - 4:40:23 PM
Last modification on : Thursday, January 13, 2022 - 11:52:04 AM
Long-term archiving on: : Saturday, June 6, 2020 - 5:01:29 PM


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Jean-Christophe Foltête, Jens Ingensand, Nicolas Blanc. Coupling crowd-sourced imagery and visibility modelling to identify landscape preferences at the panorama level. Landscape and Urban Planning, Elsevier, 2020, 197, pp.103756. ⟨10.1016/j.landurbplan.2020.103756⟩. ⟨hal-02462393⟩



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