Environmental developments create an ever increasing need for monitoring and protection measures. These efforts are often based on digital or technical solutions like data analyses or modelling. Yet, in order to enable effective, reliable and large scale environmental monitoring and protection techniques, large information volumes are required.
In a recently published paper, we analyse different integration workflows for bird observation images. Multiple data sources are combined to create a larger and temporally, geographically and semantically more extensive and complete dataset. The workflows leverage the power of deep learning image analyses models in combination with user generated information and citizen science projects.
A subsequent quality analyses revealed that the integration of social media images not only made a large impact in terms of data volume but also had a positive effect on data quality.
Hartmann, M. C., Schott, M. , Dsouza, A., Metz, Y., Volpi, M. and Purves, R. S. (2022): 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, Ecological Informatics vol 71, https://doi.org/10.1016/j.ecoinf.2022.101782 (open access)
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Lee, H., Seo, B., Cord, A.F., Volk, M., and Lautenbach, S. 2022. Using crowdsourced images to study selected cultural ecosystem services and their relationships with species richness and carbon sequestration. Ecosystem Services 54
Lee, H., Seo, B., Koellner, T., and Lautenbach, S. 2019. Mapping cultural ecosystem services 2.0 – Potential and shortcomings from unlabeled crowd sourced images. Ecological Indicators 96: 505–515