On the impact of Citizen Science-derived data quality on deep learning based classification in marine images.
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Jones, Daniel O. B.
Nattkemper, Tim W.
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The evaluation of large amounts of digital image data is of growing importance for biology, including for the exploration and monitoring of marine habitats. However, only a tiny percentage of the image data collected is evaluated by marine biologists who manually interpret and annotate the image contents, which can be slow and laborious. In order to overcome the bottleneck in image annotation, two strategies are increasingly proposed: “citizen science” and “machine learning”. In this study, we investigated how the combination of citizen science, to detect objects, and machine learning, to classify megafauna, could be used to automate annotation of underwater images. For this purpose, multiple large data sets of citizen science annotations with different degrees of common errors and inaccuracies observed in citizen science data were simulated by modifying “gold standard” annotations done by an experienced marine biologist. The parameters of the simulation were determined on t.....
Issue6, Article e0218086
Sustainable Development Goals (SDG)14.a
Essential Ocean Variables (EOV)N/A
DOI Originalhttps://doi. org/10.1371/journal.pone.0218086
CitationLangenkamper, D., Simon-Lledo, E.,Hosking, B., Jones, D.O.B. and Nattkemper, T.W. (2019) On the impact of Citizen Science-derived data quality on deep learning based classification in marine images. PLoS ONE 14(6): e021808, 16pp. DOI: https://doi. org/10.1371/journal.pone.0218086
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