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.
Highlights
Peoples’ individual interactions with the environment, an important methodological factor from an ecosystem service (ES) modelling perspective[19,20,21]
Individual Flickr images (Fig. 1a) are passed through the Places365-ResNet-50 model to generate a grid cell mean for 365 scene classes (Fig. 1b) and 102 SUN image attributes scores (Fig. 1c), while image scenicness scores generated by the SoN ResNet are used to produce a normalised rating distribution between 1 and 10 (Fig. 1d)
In an ES context, social media provides a rich new source of data to capture the cultural contributions of ecosystems to human well-being but its use is rarely v alidated[46]
Summary
Peoples’ individual interactions with the environment, an important methodological factor from an ES modelling perspective[19,20,21]. Subsequent research has focused on the detection of attribute groups co-influencing the perception of s cenicness[42], the discovery of new attributes using ancillary text c orpora[43], and the relationship between scenicness and land cover as observed by remote sensing s atellites[44] These studies demonstrate the potential of modelling landscape aesthetics using social media and deep learning. From an ES modelling perspective, social media provides the possibility of integrating peoples’ revealed preferences through their spatial interactions with the environment, and to observe the aesthetic contributions of landscapes with high spatial and temporal g ranularity[45] This is in contrast to indicator-based models, which only take into account a general set of stated preferences, are limited by their spatial resolution and rely on updates to the underlying datasets to track temporal changes. Our findings illustrate how these innovative methods can advance ES modelling to achieve sustainable policy goals
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