Welcome to SUBSIM
- the Swedish platform for subsea image analysis
The quickly rising demand to monitor the ocean with autonomous techniques and computer vision methods led to the development of SUBSIM, a platform for efficient management and processing of underwater image and video data. SUBSIM provides essential functions to conduct research and automated monitoring with image and video surveys, including data management, machine learning, digital collaboration, citizen science, and high-performance computing.
Funding agencies





Core partners





Selected publications
You find the full publication list here
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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. In Review, Authorea. DOI: 10.22541/au.174522631.15993508/v1
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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 (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.