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

Richards, Daniel R.; Tunçer, Bige

doi:10.1016/j.ecoser.2017.09.004

Cited by 187 publications

(121 citation statements)

References 38 publications

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“…Flickr and PPGIS), and 16 models for each unique combination of the four common values (the first four values in List 1 and List 2, we compared each domestic and international user group ( n = 2), developed for each dataset). We selected the covariates based on previous research demonstrating how nature tourism is related to human infrastructure and environmental characteristics (Bagstad, Semmens, Ancona, & Sherrouse, 2016; Richards & Tunçer, 2018; Walden‐Schreiner et al, 2018). Values were modelled against nine environmental and infrastructure variables (hereafter referred to as covariates); eight continuous variables: distance from trails, roads, touristic cabins, buildings (other infrastructures, e.g.…”

Section: Methodsmentioning

confidence: 99%

Using crowdsourced spatial data from Flickr vs. PPGIS for understanding nature's contribution to people in Southern Norway

et al. 2020

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Section: Methodsmentioning

confidence: 99%

Using crowdsourced spatial data from Flickr vs. PPGIS for understanding nature's contribution to people in Southern Norway

et al. 2020

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“…The sample was derived randomly across the entire study area, in order to reflect quantitative differences in the LA-flow at the Barcelona Province scale (cf. Richards and Tunçer, 2017). The sample size and coding relied upon rigorously applied guidelines, based on former studies (Pastur et al, 2016;OterosRozas et al, 2017;Tenerelli et al, 2016) and further developed during this assessment (see Annex A).…”

Section: Mapping Landscape Aesthetics Flowmentioning

confidence: 99%

“…van Zanten et al, 2016a). Future research will also utilize computer algorithms for semiautomatic analysis of photo content, whereby computer programs are 'trained' by manual photo-sighting, such as conducted in our study (Richards and Tunçer, 2017;Kennedy et al, 2007). This will allow for the analysis of much larger photo samples and thus provide a comprehensive database for the assessment of LA-flow.…”

Section: Strengths and Shortcomings In Using Social Media Data To Assmentioning

confidence: 99%

Mapping the intangible: Using geolocated social media data to examine landscape aesthetics

2018

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A B S T R A C TThe ecosystem services concept is increasingly gaining momentum in land-use policies and landscape planning. Yet, cultural ecosystem services often lack proper assessments. With this study, we use novel methodological approaches to map the cultural ecosystem service landscape aesthetics for its enhanced consideration in land-use policies. Our study uses expert-based participatory mapping and crowd-sourced (social media) photo data to examine the spatial distribution of landscape aesthetics in the Province of Barcelona, Catalonia. We distinguish the capacity and flow of landscape aesthetics. Landscape aesthetics capacity was assessed through spatial multicriteria evaluation, consisting of a viewshed analysis and an expert-based selection and weighting of landscape features. Landscape aesthetics flow, i.e., people's actual appreciation of landscape aesthetics, was assessed by analysing a sample of 13,460 geolocated photographs from the social media platform Flickr. Our results uncover a substantial mismatch between landscape aesthetics capacity and flow. While landscape aesthetics capacity is widely distributed across the case study area, landscape aesthetics flow is (with few exceptions) mostly concentrated in urban and periurban areas. The main insights for land-use policies derived from our results are twofold. On one hand, landscape aesthetics flow seems less dependent on 'pristine nature' than experts and planners assume, while the complex integration of green and grey landscape features plays a critical role. On the other hand, urban and periurban landscapes as key landscape aesthetics providers should receive additional attention in land-use policies.

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“…For example, images from Flickr, the most prevalent online photograph sharing website, were proven to be usable by [31,32] for land cover classification and validation. Flickr was also exploited in the work of [33], who developed a novel framework for ecosystem service assessment using Google Cloud Vision and hierarchical clustering to analyse the contents of Flickr photographs automatically. Apart from Flickr, "Place Pulse 1.0", a crowdsourced image dataset created by [27], was used to predict the human judgement of a streetscape's safety [34].…”

Section: Image Recognition and Urban Analyticsmentioning

confidence: 99%

Quantifying the Characteristics of the Local Urban Environment through Geotagged Flickr Photographs and Image Recognition

Chen

Arribas‐Bel

Singleton

2020

IJGI

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Urban environments play a crucial role in the design, planning, and management of cities. Recently, as the urban population expands, the ways in which humans interact with their surroundings has evolved, presenting a dynamic distribution in space and time locally and frequently. Therefore, how to better understand the local urban environment and differentiate varying preferences for urban areas has been a big challenge for policymakers. This study leverages geotagged Flickr photographs to quantify characteristics of varying urban areas and exploit the dynamics of areas where more people assemble. An advanced image recognition model is used to extract features from large numbers of images in Inner London within the period 2013–2015. After the integration of characteristics, a series of visualisation techniques are utilised to explore the characteristic differences and their dynamics. We find that urban areas with higher population densities cover more iconic landmarks and leisure zones, while others are more related to daily life scenes. The dynamic results demonstrate that season determines human preferences for travel modes and activity modes. Our study expands the previous literature on the integration of image recognition method and urban perception analytics and provides new insights for stakeholders, who can use these findings as vital evidence for decision making.

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

Cited by 187 publications

References 38 publications

Using crowdsourced spatial data from Flickr vs. PPGIS for understanding nature's contribution to people in Southern Norway

Using crowdsourced spatial data from Flickr vs. PPGIS for understanding nature's contribution to people in Southern Norway

Mapping the intangible: Using geolocated social media data to examine landscape aesthetics

Quantifying the Characteristics of the Local Urban Environment through Geotagged Flickr Photographs and Image Recognition

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