Harnessing artificial intelligence technology and social media data to support cultural ecosystem service assessments.
Cultural Ecosystem Services (CESs), such as aesthetic and recreational enjoyment, as well as sense of place and cultural heritage, play an outstanding role in the contribution of landscapes to human well-being. Scientists, however, still often struggle to understand how landscape characteristics contribute to deliver these intangible benefits, largely because it is hard to navigate how people value nature, and because there is a lack in methods that accommodate both comprehensive and time-efficient evaluations. Recent advances in technology and the proliferation of new data sources, such as social media data, open promising alternatives to traditional, resource-intensive methods, facilitating the understanding of the multiple relationships between people and nature. Here, we examine a user-friendly artificial intelligence (AI)-based approach for inferring visual-sensory landscape values from Flickr data, combining computer vision with text mining. We show it is possible to automatically relate photographers' preferences in capturing landscape elements to a set of CESs (aesthetic value, outdoor recreation, cultural heritage, symbolic species) with reasonable accuracy, using the semantic content provided by approximately 640,000 artificially generated tags of photographs taken in the UNESCO world heritage site 'The Dolomites' (Italy). We used the geographic information in the data to demonstrate that these preferences can be further linked to different natural and human variables and be used to spatially predict CES patterns. Over 90% of photograph tags could be linked to four CES categories with reasonable confidence (accuracy ration ~ 80%). The Dolomites are highly appreciated for its aesthetic value (66% of images classified to that category) and vast cultural heritage (13%), followed by its outdoor recreation opportunities (11%) and symbolic species (10%). CES benefiting hotspots were found in areas with high tourism development and close to residential areas, and could largely be explained by a combination of environmental (e.g. landscape composition) and infrastructural (e.g. accessibility) variables. We conclude that online available AI technology and social media data can effectively be used to support rapid, flexible and transferrable CES assessments. Our work can provide a reference for innovative adaptive management approaches that can harness emerging technologies to gain insights into human-nature relationships and to sustainably manage our environment.