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Verfasst von:Hartmann, Maximilian [VerfasserIn]   i
 Schott, Moritz [VerfasserIn]   i
 Dsouza, Alishiba [VerfasserIn]   i
 Metz, Yannick [VerfasserIn]   i
 Volpi, Michele [VerfasserIn]   i
 Purves, Ross S. [VerfasserIn]   i
Titel:A text and image analysis workflow using citizen science data to extract relevant social media records
Titelzusatz:Combining red kite observations from Flickr, eBird and iNaturalist
Verf.angabe:Maximilian C. Hartmann, Moritz Schott, Alishiba Dsouza, Yannick Metz, Michele Volpi, Ross S. Purves
E-Jahr:2022
Jahr:28 August 2022
Umfang:12 S.
Fussnoten:Gesehen am 08.11.2022
Titel Quelle:Enthalten in: Ecological informatics
Ort Quelle:Amsterdam [u.a.] : Elsevier, 2006
Jahr Quelle:2022
Band/Heft Quelle:71(2022), Artikel-ID 10178, Seite 1-12
ISSN Quelle:1878-0512
Abstract:There is an urgent need to develop new methods to monitor the state of the environment. One potential approach is to use new data sources, such as User-Generated Content, to augment existing approaches. However, to date, studies typically focus on a single date source and modality. We take a new approach, using citizen science records recording sightings of red kites (Milvus milvus) to train and validate a Convolutional Neural Network (CNN) capable of identifying images containing red kites. This CNN is integrated in a sequential workflow which also uses an off-the-shelf bird classifier and text metadata to retrieve observations of red kites in the Chilterns, England. Our workflow reduces an initial set of more than 600,000 images to just 3065 candidate images. Manual inspection of these images shows that our approach has a precision of 0.658. A workflow using only text identifies 14% less images than that including image content analysis, and by combining image and text classifiers we achieve almost perfect precision of 0.992. Images retrieved from social media records complement those recorded by citizen scientists spatially and temporally, and our workflow is sufficiently generic that it can easily be transferred to other species.
DOI:doi:10.1016/j.ecoinf.2022.101782
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.1016/j.ecoinf.2022.101782
 Volltext: https://www.sciencedirect.com/science/article/pii/S1574954122002321
 DOI: https://doi.org/10.1016/j.ecoinf.2022.101782
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Convolutional neural networks
 Data integration
 Image content analysis
 User-generated content
 Volunteered geographic information
K10plus-PPN:1821159667
Verknüpfungen:→ Zeitschrift

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