Best practices for AI-based image analysis applications in aquatic sciences: The iMagine case study

Best practices for AI-based image analysis applications in aquatic sciences: The iMagine case study

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...

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Main Authors: Elnaz Azmi, Khadijeh Alibabaei, Valentin Kozlov, Tjerk Krijger, Gabriele Accarino, Sakina-Dorothée Ayata, Amanda Calatrava, Marco Mariano De Carlo, Wout Decrop, Donatello Elia, Sandro Luigi Fiore, Marco Francescangeli, Jean-Olivier Irisson, Rune Lagaisse, Martin Laviale, Antoine Lebeaud, Carolin Leluschko, Enoc Martínez, Germán Moltó, Igor Ruiz Atake, Antonio Augusto Sepp Neves, Damian Smyth, Jesús Soriano-González, Muhammad Arabi Tayyab, Vanessa Tosello, Álvaro López García, Dick Schaap, Gergely Sipos
Format: Article
Language:English
Published: Elsevier 2025-11-01
Series:Ecological Informatics
Subjects:
Machine learning
Deep learning
Computer vision
Image processing
FAIR data
Open science
Online Access:http://www.sciencedirect.com/science/article/pii/S1574954125003152
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