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Imaging data and services for aquatic science
https://doi.org/10.3030/101058625
https://www.imagine-ai.eu/
Originele titel: Imaging data and services for aquatic science (iMagine)
Identifier financieringsorganisatie: 101058625 (EU contract id)
Hoofdfinancieringscodes: 3860 - Horizon Europe - European Innovation Council (EIC)
Acroniem: iMagine
Periode: September 2022 tot Augustus 2025
Status: Afgelopen

Thesaurustermen Aquatische biologie; Artificial intelligence; Artificiële intelligentie niet elders geclassificeerd; Bio-informatica data-integratie en netwerkbiologie; Computationele biomodellering en machine learning; Fytoplankton; Machine learning; Mariene ecologie; Onderwaterakoestiek
 Instituten | Publicaties | URL's 
Instituten (8)  Top | Publicaties | URL's 
  • Stichting European Grid Initiative (EGI.eu), meer, coördinator
  • Euro-Mediterranean Center on Climate Change (CMCC), meer, partner
  • Spanish Council for Scientific Research (CSIC), meer, partner
  • Sorbonne Université, meer
  • University of Trento, meer
  • Technical University of Valencia, meer
  • Vlaams Instituut voor de Zee (VLIZ), meer
  • Karlsruhe Institute of Technology, meer
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.
Publicaties (5)  Top | Instituten | URL's 
  • 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, meer
  • Decrop, W.; Lagaisse, R.; Mortelmans, J.; Muñiz, C.; Heredia, I.; Calatrava, A.; Deneudt, K. (2025). Automated image classification workflow for phytoplankton monitoring. Front. Mar. Sci. 12: 1699781. https://dx.doi.org/10.3389/fmars.2025.1699781, meer
  • Azmi, E.; Alibabaei, K.; Kozlov, V.; Krijger, T.; Accarino, G.; Ayata, S.-D.; Calatrava, A.; De Carlo, M.M.; Decrop, W.; Elia, D.; Fiore, L.; Francescangeli, M.; Irisson, J.-O.; Lagaisse, R.; Laviale, M.; Lebeaud, A.; Leluschko, C.; Martínez, E.; Moltó, G.; Atake, I.R.; Antonio Augusto, S.N.; Damian, S.; Jesus, S.-G.; Tayyab, M.A.; Tosello, V.; López García, A.; Schaap, D.; Sipos, G. (2025). Best practices for AI-based image analysis applications in aquatic sciences: The iMagine case study. Ecological Informatics 91: 103306. https://dx.doi.org/10.1016/j.ecoinf.2025.103306, meer
  • Calonge, A.; Develter, R.; Muñiz, C.; Parcerisas, C.; Reubens, J.; Boone, W.; Deneudt, K.; Debusschere, E. (2026). Scalable low-cost seabed landers: The missing link for sustained, integrated, long-term observations in dynamic shallow seas. Remote Sensing in Ecology and Conservation Online first: 1-9. https://dx.doi.org/10.1002/rse2.70072, meer
  • Decrop, W.; Deneudt, D.; Parcerisas, C.; Schall, E.; Debusschere, E. (2025). Transfer learning for distance classification of marine vessels using underwater sound. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 18: 19710-19726. https://dx.doi.org/10.1109/jstars.2025.3593779, meer
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