Using image recognition to automate assessment of cultural ecosystem services from social media photographs

Daniel R. Richards and Bige Tunçer

Ecosystem Services, 2018, vol. 31, issue PC, 318-325

Abstract: Quantifying and mapping cultural ecosystem services is complex because of their intangibility. Data from social media, such as geo-tagged photographs, have been proposed for mapping cultural use or appreciation of ecosystems. However, manual content analysis and classification of large numbers of photographs is time consuming. This study develops a novel method for automating content analysis of social media photographs for ecosystem services assessment. The approach applies an online machine learning algorithm – Google Cloud Vision – to analyse over 20,000 photographs from Singapore, and uses hierarchical clustering to group these photographs. The accuracy of the classification was assessed by comparison with manual classification. Over 20% of photographs were taken of nature, being of animals or plants. The distribution of nature photographs was concentrated around particular natural attractions, and nature photographs were more likely to occur in parks and areas of high vegetation cover. The approach developed for clustering photographs was accurate and saved approximately 170h of manual work. The method provides an indicator of cultural ecosystem services that can be applied rapidly over large areas. Automated assessment and mapping of cultural ecosystem services could be used to inform urban planning.

Keywords: Machine learning; Recreational ecosystem services; Singapore; Urban ecology; Recreation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (44)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecoser:v:31:y:2018:i:pc:p:318-325

DOI: 10.1016/j.ecoser.2017.09.004

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