Automated photo content classification of Instagram posts to identify patterns of human uses in peri-urban protected areas: A case study from Vienna, Austria

Blue and green spaces in cities provide essential ecosystem services to their inhabitants, including recreational and experiential opportunities. Their importance became further highlighted during the COVID-19 pandemic as urbanites sought to relieve some of the associated pressure. However, urban ecosystems are threatened by degradation and pollution, but also by other activities, including recreation. In this context, protected areas face the challenge of balancing visitor interests with conservation objectives, particularly in peri-urban areas. Social media provides an opportunity to analyse human activities in such areas. This study investigates spatial and temporal patterns in Instagram photos at three case study sites in Vienna, Lainzer Tiergarten , Lobau, and Nussberg with different protection statuses between 2018 and 2022. Automated content labelling using Google's Cloud Vision API and subsequent classification identified 19 clusters from 54,751 downloaded photos. Seasonal variations were observed, such as the prevalence of Plant and Insect photos in spring and summer, and Landscape content in autumn and winter. The COVID-19 pandemic coincided with and contributed to an increase in user activity, but seasonal trends were unaffected. Site-specific patterns also emerged, with Panoramas dominating in Nussberg , the Riverscape characterising Lobau , and Woodlands dominating in Lainzer Tiergarten . Our findings demonstrate that automated social media photo content analysis can capture spatial and temporal variations in visitor behaviour and landscape preferences, providing valuable insights for targeted visitor management and the establishment of conservation strategies in peri-urban ecosystems. Integrating these analyses with other methods, such as surveys or mobile phone tracking, can provide a more comprehensive understanding of human-environment interactions. • Classification of almost 55,000 photo post contents from three protected areas in Vienna. • Content patterns reflect the unique characteristics of each protected area. • Increased use of peri-urban green and blue spaces for recreation during COVID-19 pandemic. • Findings provide valuable insights for conservation management and visitor planning.

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Publication Details

Journal
Urban forestry & urban greening
Published
2025-08-25
DOI
https://doi.org/10.1016/j.ufug.2025.129029
Citations
1
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
1.11

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article

Automated photo content classification of Instagram posts to identify patterns of human uses in peri-urban protected areas: A case study from Vienna, Austria

Rafaela Schinegger, Nina Kaiser, Stefan Stoll, Nur Banu Özcelik et al.
1 citations
Urban forestry & urban greening
Land Use and Ecosystem Services
1.11
article

Automated photo content classification of Instagram posts to identify patterns of human uses in peri-urban protected areas: A case study from Vienna, Austria

Rafaela Schinegger, Nina Kaiser, Stefan Stoll, Nur Banu Özcelik, Martin Palt, Alexander Huber, Christa Hainz-Renetzeder, Florian Borgwardt, Alice Wanner, Friedrich Leisch, Michael Binder, Lena Ortega Menjivar
article en
1 citations

Abstract

Blue and green spaces in cities provide essential ecosystem services to their inhabitants, including recreational and experiential opportunities. Their importance became further highlighted during the COVID-19 pandemic as urbanites sought to relieve some of the associated pressure. However, urban ecosystems are threatened by degradation and pollution, but also by other activities, including recreation. In this context, protected areas face the challenge of balancing visitor interests with conservation objectives, particularly in peri-urban areas. Social media provides an opportunity to analyse human activities in such areas. This study investigates spatial and temporal patterns in Instagram photos at three case study sites in Vienna, Lainzer Tiergarten , Lobau, and Nussberg with different protection statuses between 2018 and 2022. Automated content labelling using Google's Cloud Vision API and subsequent classification identified 19 clusters from 54,751 downloaded photos. Seasonal variations were observed, such as the prevalence of Plant and Insect photos in spring and summer, and Landscape content in autumn and winter. The COVID-19 pandemic coincided with and contributed to an increase in user activity, but seasonal trends were unaffected. Site-specific patterns also emerged, with Panoramas dominating in Nussberg , the Riverscape characterising Lobau , and Woodlands dominating in Lainzer Tiergarten . Our findings demonstrate that automated social media photo content analysis can capture spatial and temporal variations in visitor behaviour and landscape preferences, providing valuable insights for targeted visitor management and the establishment of conservation strategies in peri-urban ecosystems. Integrating these analyses with other methods, such as surveys or mobile phone tracking, can provide a more comprehensive understanding of human-environment interactions. • Classification of almost 55,000 photo post contents from three protected areas in Vienna. • Content patterns reflect the unique characteristics of each protected area. • Increased use of peri-urban green and blue spaces for recreation during COVID-19 pandemic. • Findings provide valuable insights for conservation management and visitor planning.

Urban forestry & urban greeningVol. 113
Landscape Institute (GB), Trier University of Applied Sciences (DE), Aquatic Systems (United States) (US), Hessian Agency for Nature Conservation, Environment and Geology (DE), Nature Conservation Agency (LV), University of Duisburg-Essen (DE), BOKU University (AT)
Universität für Bodenkultur Wien, Hochschuljubiläumsstiftung der Stadt Wien
Sustainable cities and communities
Openalex Percentile: Top 20%
Land Use and Ecosystem Services
1.11
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