A text and image analysis workflow using citizen science data to extract relevant social media records: Combining red kite observations from Flickr, eBird and iNaturalist
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 app...
Gespeichert in:
| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
28 August 2022
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| In: |
Ecological informatics
Year: 2022, Jahrgang: 71, Pages: 1-12 |
| ISSN: | 1878-0512 |
| DOI: | 10.1016/j.ecoinf.2022.101782 |
| Online-Zugang: | Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.ecoinf.2022.101782 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S1574954122002321 
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| Verfasserangaben: | Maximilian C. Hartmann, Moritz Schott, Alishiba Dsouza, Yannick Metz, Michele Volpi, Ross S. Purves |
MARC
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| 520 | |a 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. | ||
| 650 | 4 | |a Convolutional neural networks | |
| 650 | 4 | |a Data integration | |
| 650 | 4 | |a Image content analysis | |
| 650 | 4 | |a User-generated content | |
| 650 | 4 | |a Volunteered geographic information | |
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| 700 | 1 | |a Metz, Yannick |e VerfasserIn |4 aut | |
| 700 | 1 | |a Volpi, Michele |e VerfasserIn |4 aut | |
| 700 | 1 | |a Purves, Ross S. |e VerfasserIn |4 aut | |
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