Best practices for AI-based image analysis applications in aquatic sciences: The iMagine case study
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
In: Ecological Informatics. Elsevier: Amsterdam. ISSN 1574-9541; e-ISSN 1878-0512, more
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| Author keywords |
Machine learning; Deep learning; Computer vision; Image processing; FAIR data; Open science; Aquatic sciences; Ocean and marine sciences; Automated species classification |
| Project | Top | Authors |
- Imaging data and services for aquatic science, more
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| Authors | | Top |
- Azmi, E.
- Alibabaei, K.
- Kozlov, V.
- Krijger, T., more
- Accarino, G.
- Ayata, S.-D.
- Calatrava, A.
- De Carlo, M.M.
- Decrop, W., more
- Elia, D.
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- Fiore, L.
- Francescangeli, M.
- Irisson, J.-O.
- Lagaisse, R., more
- Laviale, M.
- Lebeaud, A.
- Leluschko, C.
- Martínez, E.
- Moltó, G.
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- Atake, I.R.
- Antonio Augusto, S.N.
- Damian, S.
- Jesus, S.-G.
- Tayyab, M.A.
- Tosello, V.
- López García, A.
- Schaap, D., more
- Sipos, G.
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| Abstract |
The iMagine project is an EU-funded initiative led by the EGI Foundation. One of the objectives of this project is to provide an AI platform that leverages AI-powered tools to improve the processing and analysis of imaging data from marine and freshwater ecosystems, contributing to the study of the health of oceans, seas, coasts, and inland waters. Connected to the European Open Science Cloud (EOSC), iMagine supports the development, training, and deployment of AI models by collaborating with twelve use cases across diverse aquatic science fields. This collaboration fosters valuable insights and accelerates scientific progress by refining existing solutions in data acquisition, preprocessing, and model deployment. The platform offers trained models as a service, integrating AI tools for image annotation, ensuring the creation of high-quality datasets that comply with FAIR principles. Through these methodologies, iMagine enhances consistency, enabling researchers to efficiently publish and share data in repositories. |
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