Original title: Imaging data and services for aquatic science (iMagine)
Principal funding codes: 3860 - Horizon Europe - European Innovation Council (EIC)
Acronym: iMagine
Period: September 2022 till August 2025
Status: In Progress
Thesaurus terms Aquatic biology; Artificial intelligence; Artificial intelligence not elsewhere classified; Bioinformatics data integration and network biology; Computational biomodelling and machine learning; Machine learning; Marine ecology; Phytoplankton; Underwater acoustics
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| Institutes (6) |
Top | Publications | Datasets |
- Stichting European Grid Initiative (EGI.eu), more, co-ordinator
- Spanish Council for Scientific Research (CSIC), more, partner
- Sorbonne Université, more
- University of Trento, more
- Technical University of Valencia, more
- Vlaams Instituut voor de Zee (VLIZ), more
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| Abstract |
| iMagine provides a portfolio of ‘free at point of use’ image datasets, high-performance image analysis tools empowered with Artificial Intelligence (AI), and Best Practice documents for scientific image analysis. These services and materials enable better and more efficient processing and analysis of imaging data in marine and freshwater research, accelerating our scientific insights about processes and measures relevant for healthy oceans, seas, coastal and inland waters. By building on the compute platform of the European Open Science Cloud (EOSC) the project delivers a generic framework for AI model development, training, and deployment, which can be adopted by researchers for refining their AI based applications for water pollution mitigation, biodiversity and ecosystem studies, climate change analysis and beach monitoring, but also for developing and optimising other AI based applications in this field. The iMagine compute layer consists of providers from the pan-European EGI federation-infrastructure, collectively offering over 132,000 GPU-hours, 6,000,000 CPU-hours and 1500 TB-month for image hosting and processing. The iMagine AI framework offers neural networks, parallel post-processing of very large data, and analysis of massive online data streams in distributed environments. 12 RIs will share over 9 million images and 8 AI-powered applications through the framework. Having representatives of so many RIs and IT institutes, developing a portfolio of eye-catching image processing services together will also give rise to Best Practices. The ynergies between aquatic use cases will lead to common solutions in data management, quality control, performance, integration, provenance, and FAIRness, contributing to harmonisation across RIs and providing input for the iMagine Best Practice guidelines. The project results will be integrated into and will bring important contributions from RIs and e-infrastructures to EOSC and AI4EU. |
| Datasets (2) |
Top | Institutes | Publications |
- Decrop, W., Lagaisse, R., Jonas, M., Muyle, J., Amadei Martínez, L., & Deneudt, K. (2024). LifeWatch observatory data: phytoplankton annotated trainingset by FlowCam imaging in the Belgian Part of the North Sea (Versie v1). Zenodo. https://doi.org/10.5281/zenodo.10554845, more
- Lagaisse, Rune, R.; Amadei Martinez, Luz, L.; Decrop, Wout, W.; Deneudt, Klaas, K.; Muyle, Julie, J.; Mortelmans, Jonas, J.; Flanders Marine Institute (VLIZ): Belgium; (2024): LifeWatch observatory data: phytoplankton annotated image library by FlowCam imaging for the Belgian part of the North Sea. Marine Data Archive., more
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| Publications (2) |
Top | Institutes | Datasets |
- Lagaisse, R.; Decrop, W.; Deneudt, K. (2024). AI in marine sciences: an open-access integrated environment for automated classification of phytoplankton images, in: Mees, J. et al. Book of abstracts – VLIZ Marine Science Day, 6 March 2024, Oostende. VLIZ Special Publication, 91: pp. 82, more
- Flanders Marine Institute (VLIZ) (2024). Multipurpose seabed moorings: Developed for coastal dynamic seas. Oceanography Suppl. : In prep., more
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