Geo-Data for Mapping Scenic Beauty: Exploring the Potential of Remote Sensing and Social Media

Havinga, Ilan; Marcos, Diego; Bogaart, P.W.; Hein, Lars; Tuia, Devis

doi:10.1109/igarss47720.2021.9553417

Cited by 5 publications

(7 citation statements)

References 13 publications

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“…Nevertheless, the data that are available through social media do make it possible to untangle some of this cultural variation. For example, the text data associated with Flickr images can contain quite specific motivations for people's interactions (Havinga et al, 2021b). At the same time, social media also offers the opportunity to examine collective experiences through the presence of virtual communities (Langemeyer & Calcagni, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, the model's most confident predictions remained consistent. To further validate its predictions, the image tags and descriptions could be used which often contain information related to the purpose of the image (Havinga et al, 2021b). To address this issue, a more conservative β coefficient may also be utilised, as the β = 0.1 model revealed.…”

Section: Predicting Cultural Preferences For Biodiversity Using Deep ...mentioning

confidence: 99%

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Social media and deep learning reveal specific cultural preferences for biodiversity

et al. 2023

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Social media has created new opportunities to map cultural ecosystem services (CES) related to biodiversity at large scales. However, using these novel data to understand people's preferences in relation to these CES remains a challenge. To address this, we trained a deep learning model to capture people's interactions with selected flora and fauna on Flickr as a cultural service related to biodiversity and compared this with citizen science data on iNaturalist, with photos of individual species considered as human–species interactions. After mapping the distribution of people's interactions in Great Britain on Flickr and iNaturalist, we find significant spatial differences in people's preferences on the two platforms. Using a second, pretrained deep learning model, we were also able to identify different preferences for species groups such as birds on social media versus citizen science. To better understand people's preferences, we also compared people's interactions with species richness and abundance for a group of 36 bird species, sometimes finding large differences between people's interactions and these ecological measures. Our findings demonstrate that social media can be used to include a wider range of preferences in CES assessments along‐side citizen science data. However, these preferences reflect only a limited first‐hand experience of biodiversity. Read the free Plain Language Summary for this article on the Journal blog.

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Section: Discussionmentioning

confidence: 99%

Section: Predicting Cultural Preferences For Biodiversity Using Deep ...mentioning

confidence: 99%

Social media and deep learning reveal specific cultural preferences for biodiversity

et al. 2023

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“…Another application is environmental monitoring, either by mapping animal and plant species [99] or by assessing landscape preferences [100] and aesthetics [81], [101]. Urban areas can especially benefit from this data by collecting citizen sentiments and opinions from social media image content and textual descriptions [102].…”

Section: B Methodsmentioning

confidence: 99%

Geoinformation Harvesting From Social Media Data: A community remote sensing approach

Zhu

Wang

Kochupillai

et al. 2022

IEEE Geosci. Remote Sens. Mag.

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As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multiperspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysisready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.

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“…Alternatively, the use of user-generated metadata associated with Flickr images, such as tags and descriptions could also be used to confirm prediction accuracy (Havinga et al, 2021b).…”

Section: Bird Biodiversity and Conservationmentioning

confidence: 99%

“…Nevertheless, the data that is available through social media does make it possible to untangle some of this cultural variation. For example, the text data associated with Flickr images can contain quite specific motivations for peoples' interactions (Havinga et al, 2021b). At the same time, social media also offers the opportunity to examine collective experiences through the presence of virtual communities (Langemeyer and Calcagni, 2022).…”

Section: Large-scale Ces Assessmentmentioning

confidence: 99%

Crowdsourced data and machine learning to measure cultural ecosystem services

Havinga

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Cultural ecosystem services (CES) are some of the most valuable contributions of ecosystems to human well-being. Nevertheless, these services are often underrepresented in ecosystem service assessments. Defining CES for the purposes of spatial quantification has been challenging because it has been difficult to spatially model CES. However, rapid increases in mobile network connectivity and the use of social media have generated huge amounts of crowdsourced data. This offers an opportunity to define and spatially quantify CES. We inventoried established CES conceptualisations and sources of crowdsourced data to propose a CES definition and typology for spatial quantification. Furthermore, we present the results of three spatial models employing crowdsourced data to measure CES on Texel, a coastal island in the Netherlands. Defining CES as information-flows best enables service quantification. A general typology of eight services is proposed. The spatial models produced distributions consistent with known areas of cultural importance on Texel. However, user representativeness and measurement uncertainties affect our results.Ethical considerations must also be taken into account. Still, crowdsourced data is a valuable source of information to define and model CES due to the level of detail available. This can encourage the representation of CES in ecosystem service assessments.

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Geo-Data for Mapping Scenic Beauty: Exploring the Potential of Remote Sensing and Social Media

Cited by 5 publications

References 13 publications

Social media and deep learning reveal specific cultural preferences for biodiversity

Social media and deep learning reveal specific cultural preferences for biodiversity

Geoinformation Harvesting From Social Media Data: A community remote sensing approach

Crowdsourced data and machine learning to measure cultural ecosystem services

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