Data
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Nilsson C (2024). Koster historical biodiversity assessment. Version 1.0. Wildlife.ai. Occurrence dataset, https://doi.org/10.15468/rzhmef accessed via GBIF.org on 2024-09-01.
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Green L, Svensson L, Burman E, Germishuys J, Anton V, Obst M (2024). Eight-fjords shallow underwater videos. Version 1.2. Wildlife.ai. Occurrence dataset https://doi.org/10.15468/8m29p6 accessed via GBIF.org on 2024-04-08.
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Obst M, Al-Khateeb S, Anton V, & Germishuys J (2023). Synthetic images of corals (Desmophyllum pertusum) with object detection models (Version 1) Data publication, University of Gothenburg. https://doi.org/10.5878/hp35-4809.
Models
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Fiorina L, Obst M, Sundelöf A, Ljungberg P, Sande H (2025) Object detection model (RF-DETR based) for European lobster monitoring in Swedish coastal waters. Zenodo. https://zenodo.org/records/15755673
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Dalipi X, Burman E., Germishuys J, Anton V, & Obst M (2025). Object detection model for epibenthic species in low-trophic aquacultures (LTA): A case study on the Swedish west coast. Zenodo. https://doi.org/10.5281/zenodo.14713926
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Nilsson C, Germishuys J, Burman E, Anton V, White J, & Obst M (2024). Koster historical invertebrate model - SUBSIM 17tx. Zenodo. https://doi.org/10.5281/zenodo.13589902
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Green L, Burman E, Obst M, & Germishuys J (2024) Reference Model - GU Goby 4sp. Zenodo. https://doi.org/10.5281/zenodo.10932673
Scientific articles
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Nilsson C, Faurby S, Burman E, Germishuys J, Obst M (2025) Applying deep learning to quantify drivers of long-term ecological change in a Swedish marine protected area. Ecology and Evolution, 15:e72091, https://doi.org/10.1002/ece3.72091
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Borremans C, et al (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782.
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Al-Khateeb S, Obst M, Anton V, Germishuys (2024) A methodology to detect deepwater corals using Generative Adversarial Networks. In Submission.
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Semenov A, Zhang Y, Ponti M (2022) Who will stay Using Deep Learning to predict engagement of citizen scientists. ArXiv, 2204.14046. https://doi.org/10.48550/arXiv.2204.14046.
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Anton V, Germishuys J, Bergström P, Lindegarth M, Obst M (2021) An open-source, citizen science and machine learning approach to analyse subsea movies. Biodiversity Data Journal https://bdj.pensoft.net/article/60548/.
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Guidi et al (2020) Big data in marine science. Marine Board Future Science Brief, 6. European Marine Board: Ostend. ISBN 9789492043931. 50 pp. https://dx.doi.org/10.5281/zenodo.3755793.
Scientific reports
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Fiorina L (2025) AI-powered monitoring of European lobsters in West Sweden - A Computer Vision Approach. MSc thesis report, University of Gothenburg.
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de Jesús Álvarez PI (2025) Studying biodiversity and distribution of benthic vegetation with deep learning methods. MSc thesis report, University of Gothenburg.
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Krzysztof AF & Ziach J (2024) Computer vision for marine life detection in unconstrained underwater environments in New Zealand. BSc thesis report, IT University of Copenhagen, Denmark.
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Dalipi X (2024) Deep dive into Low-trophic aquacultures: Assessing Benthic Biodiversity using Computer Vision & Deep Learning. MSc thesis report, University of Gothenburg.
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Nilsson C (2024) Depth learning - Using Deep-Learning Object Detection Software to Investigate Spatiotemporal Vertical Ecological Trends on a Submarine Canyon Wall in Northern Skagerrak. MSc thesis report, University of Gothenburg.
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Alsterlind S (2024) Application of AI for Monitoring of Sessile Biodiversity using Segmentation Detection in Underwater Imagery. BSc thesis report, University of Gothenburg.
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Persson F (2023) Assessing the influence of a submarine canyon on elasmobranch diversity and abundance with baited underwater video systems (BRUVs) and machine-learning. BSc thesis report, University of Gothenburg.
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Thorslund R (2022) En studie om trådningens påverkan på sjöpennor i Kattegatt. BSc thesis report, University of Gothenburg.
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Kokk JH (2021) Testing a machine-learning model for species identification of deep water fauna - Applications in Kosterhavet National Park. BSc thesis report, University of Gothenburg.
Posters
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Nilsson C (2023) Depth learning – Using machine-learning to estimate vertical zonation of a submarine canyon in Northern Skagerrak. Poster at conference ”Towards data-driven ecology”
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Obst M, Germishuys J, Burman E, Browaldh E, Anton V, Linders T (2023) SUBSIM - a national platform for SUBSeaIMage analysis. Poster at conference “Marine environmental Monitoring for Future Innovation and Sustainability (MEMFIS)”.
