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An open‐source image classifier for characterizing recreational activities across landscapes

Winder, Samantha G. ; Lee, Heera 1; Seo, Bumsuk 1; Lia, Emilia H.; Wood, Spencer A.
1 Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Environmental management increasingly relies on information about ecosystem services for decision-making. Compared with regulating and provisioning services, cultural ecosystem services (CES) are particularly challenging to characterize and measure at management-relevant spatial scales, which has hindered their consideration in practice.
Social media are one source of spatially explicit data on where environments support various types of CES, including physical activity. As tools for automating social media content analysis with artificial intelligence (AI) become more commonplace, studies are promoting the potential for AI and social media to provide new insights into CES. Few studies, however, have evaluated what biases are inherent to this approach and whether it is truly reproducible.
This study introduces and applies a novel and open-source convolutional neural network model that uses computer vision to recognize recreational activities in the content of photographs shared as social media. We train a model to recognize 12 common recreational activities to map one aspect of recreation in a national forest in Washington, USA, based on images uploaded to Flickr.
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Verlagsausgabe §
DOI: 10.5445/IR/1000148790
Veröffentlicht am 19.07.2022
Originalveröffentlichung
DOI: 10.1002/pan3.10382
Scopus
Zitationen: 4
Web of Science
Zitationen: 5
Dimensions
Zitationen: 6
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 10.2022
Sprache Englisch
Identifikator ISSN: 2575-8314
KITopen-ID: 1000148790
HGF-Programm 12.11.21 (POF IV, LK 01) Natural ecosystems as sources and sinks of GHGs
Weitere HGF-Programme 12.11.35 (POF IV, LK 01) Tailored information for users and stakeholders
Erschienen in People and Nature
Verlag John Wiley and Sons
Band 4
Heft 5
Seiten 1249-1262
Vorab online veröffentlicht am 17.07.2022
Schlagwörter convolutional neural network, cultural ecosystem services, environmental management, image recognition, machine learning, open source, recreational activities, social media
Nachgewiesen in Web of Science
Scopus
Dimensions
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