European Ocean Biodiversity Information System
[ report an error in this record ]basket (0): add | show Print this page
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
Peer reviewed article  
Available in  Authors 
    Vlaams Instituut voor de Zee: Open access 416279 [ download pdf ]
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
Authors  Top 
  • 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.
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.
All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy Top | Authors