Preprints
https://doi.org/10.5194/essd-2026-614
https://doi.org/10.5194/essd-2026-614
02 Sep 2026
 | 02 Sep 2026
Status: this preprint is currently under review for the journal ESSD.

A FAIR annotated set of 60k+ in situ zooplankton images from the Greater North Sea and Greenland

Pieter D. L. Hovenkamp, Lodewijk Van Walraven, Daan Temmerman, A. Frank Van der Stappen, and Dick Van Oevelen

Abstract. The North Sea is a productive shelf sea with a wide variety of hydrographic and biological conditions. It is subject to high anthropogenic pressures, including climate change, terrestrial nutrient loading, and the development of offshore wind farms, and the associated environmental changes have major consequences for zooplankton abundance and community composition. In situ imaging provides new opportunities to monitor meso- and macrozooplankton, as it enables sampling at meter-scale resolution and improved assessments of fragile gelatinous taxa. The vast amounts of data that can be collected with in situ imaging necessitate the use of automated data processing pipelines. However, developing these requires a substantial amount of expert-annotated data. In this paper, we share a large annotated set of plankton and particle images from an in situ shadowgraph imager (ISIIS-DPI, 46,331 images) and the Continuous Particle Imaging and Classification Sensor (CPICS, 14,496 images) among, respectively, 64 and 42 taxonomic and trait-based classes that were collected in the Greater North Sea and complemented with data from Greenland. Based on the geographical coverage, size distribution, and environmental data collected simultaneously with the images, we show that this set is taxonomically representative of the Greater North Sea. In addition, we trained Convolutional Neural Network classifiers, one for the ISIIS-DPI and one for the CPICS, and both achieved an F1-score of 80.1 %, showing that these classifiers can be reliably used on newly collected ISIIS-DPI and CPICS data in this region. All annotated images, as well as location and environmental data, are shared publicly (10.5281/zenodo.21737141, Hovenkamp et al., 2026c) following a flexible taxonomic classification scheme to ensure flexible use and inter-operability with similar (future) sets. The annotated data and classifiers that we present should greatly facilitate zooplankton studies employing in situ imaging in the Greater North Sea. This improves our ability to assess how zooplankton communities are likely to respond to the ongoing anthropogenic pressure on the North Sea and addresses a critical knowledge need.

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Pieter D. L. Hovenkamp, Lodewijk Van Walraven, Daan Temmerman, A. Frank Van der Stappen, and Dick Van Oevelen

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Pieter D. L. Hovenkamp, Lodewijk Van Walraven, Daan Temmerman, A. Frank Van der Stappen, and Dick Van Oevelen

Data sets

A FAIR annotated set of 60k+ in situ zooplankton images from the Greater North Sea and Greenland Pieter D. L. Hovenkamp, Lodewijk van Walraven, Daan Temmerman, A. Frank van der Stappen, and Dick van Oevelen https://doi.org/10.5281/zenodo.21737141

Pieter D. L. Hovenkamp, Lodewijk Van Walraven, Daan Temmerman, A. Frank Van der Stappen, and Dick Van Oevelen
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Short summary
In this paper, we collected millions of images of marine zooplankton across the Greater North Sea and Greenland using underwater cameras. Over 60,000 of these images were labeled, and AI was trained to automatically classify zooplankton via images. The annotated data and classifiers help us to monitor zooplankton populations more efficiently using novel imaging techniques —a vital task as climate change and construction of offshore wind farms reshape marine pelagic ecosystems.
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