Social media and deep learning capture the aesthetic quality of the landscape

Abstract Peoples’ recreation and well-being are closely related to their aesthetic enjoyment of the landscape. Ecosystem service (ES) assessments record the aesthetic contributions of landscapes to peoples’ well-being in support of sustainable policy goals. However, the survey methods available to m...

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Autores principales:	Ilan Havinga, Diego Marcos, Patrick W. Bogaart, Lars Hein, Devis Tuia
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/473e0180ad0c431c868763788681023c
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id	oai:doaj.org-article:473e0180ad0c431c868763788681023c
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spelling	oai:doaj.org-article:473e0180ad0c431c868763788681023c2021-12-02T18:37:09ZSocial media and deep learning capture the aesthetic quality of the landscape10.1038/s41598-021-99282-02045-2322https://doaj.org/article/473e0180ad0c431c868763788681023c2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99282-0https://doaj.org/toc/2045-2322Abstract Peoples’ recreation and well-being are closely related to their aesthetic enjoyment of the landscape. Ecosystem service (ES) assessments record the aesthetic contributions of landscapes to peoples’ well-being in support of sustainable policy goals. However, the survey methods available to measure these contributions restrict modelling at large scales. As a result, most studies rely on environmental indicator models but these do not incorporate peoples’ actual use of the landscape. Now, social media has emerged as a rich new source of information to understand human-nature interactions while advances in deep learning have enabled large-scale analysis of the imagery uploaded to these platforms. In this study, we test the accuracy of Flickr and deep learning-based models of landscape quality using a crowdsourced survey in Great Britain. We find that this novel modelling approach generates a strong and comparable level of accuracy versus an indicator model and, in combination, captures additional aesthetic information. At the same time, social media provides a direct measure of individuals’ aesthetic enjoyment, a point of view inaccessible to indicator models, as well as a greater independence of the scale of measurement and insights into how peoples’ appreciation of the landscape changes over time. Our results show how social media and deep learning can support significant advances in modelling the aesthetic contributions of ecosystems for ES assessments.Ilan HavingaDiego MarcosPatrick W. BogaartLars HeinDevis TuiaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ilan Havinga Diego Marcos Patrick W. Bogaart Lars Hein Devis Tuia Social media and deep learning capture the aesthetic quality of the landscape
description	Abstract Peoples’ recreation and well-being are closely related to their aesthetic enjoyment of the landscape. Ecosystem service (ES) assessments record the aesthetic contributions of landscapes to peoples’ well-being in support of sustainable policy goals. However, the survey methods available to measure these contributions restrict modelling at large scales. As a result, most studies rely on environmental indicator models but these do not incorporate peoples’ actual use of the landscape. Now, social media has emerged as a rich new source of information to understand human-nature interactions while advances in deep learning have enabled large-scale analysis of the imagery uploaded to these platforms. In this study, we test the accuracy of Flickr and deep learning-based models of landscape quality using a crowdsourced survey in Great Britain. We find that this novel modelling approach generates a strong and comparable level of accuracy versus an indicator model and, in combination, captures additional aesthetic information. At the same time, social media provides a direct measure of individuals’ aesthetic enjoyment, a point of view inaccessible to indicator models, as well as a greater independence of the scale of measurement and insights into how peoples’ appreciation of the landscape changes over time. Our results show how social media and deep learning can support significant advances in modelling the aesthetic contributions of ecosystems for ES assessments.
format	article
author	Ilan Havinga Diego Marcos Patrick W. Bogaart Lars Hein Devis Tuia
author_facet	Ilan Havinga Diego Marcos Patrick W. Bogaart Lars Hein Devis Tuia
author_sort	Ilan Havinga
title	Social media and deep learning capture the aesthetic quality of the landscape
title_short	Social media and deep learning capture the aesthetic quality of the landscape
title_full	Social media and deep learning capture the aesthetic quality of the landscape
title_fullStr	Social media and deep learning capture the aesthetic quality of the landscape
title_full_unstemmed	Social media and deep learning capture the aesthetic quality of the landscape
title_sort	social media and deep learning capture the aesthetic quality of the landscape
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/473e0180ad0c431c868763788681023c
work_keys_str_mv	AT ilanhavinga socialmediaanddeeplearningcapturetheaestheticqualityofthelandscape AT diegomarcos socialmediaanddeeplearningcapturetheaestheticqualityofthelandscape AT patrickwbogaart socialmediaanddeeplearningcapturetheaestheticqualityofthelandscape AT larshein socialmediaanddeeplearningcapturetheaestheticqualityofthelandscape AT devistuia socialmediaanddeeplearningcapturetheaestheticqualityofthelandscape
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Social media and deep learning capture the aesthetic quality of the landscape

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