Sipos, Gergely, and Dick Schaap. 2025. iMagine: Revolutionising Aquatic Sciences with AI-Driven Image Analysis. ERCIM News, no. 140 (January 21, 2025)
iMagine Publications
This page collects the publications acknowledging iMagine that were published throughout the project.
NB: the links are to journals when an Open Access version exists, or to the repository where the full texts are available.
2025
Elnaz Azmi, Khadijeh Alibabaei, Valentin Kozlov, Álvaro López García, Dick Schaap, and Gergely Sipos. 2025. IMagine: AI-Powered Image Data Analysis in Aquatic Science. In Proceedings of the Platform for Advanced Scientific Computing Conference (PASC ’25). Association for Computing Machinery, New York, NY, USA, 1–11. https://doi.org/10.1145/3732775.3733584
Elnaz Azmi, Khadijeh Alibabaei, Valentin Kozlov, Tjerk Krijger, Gabriele Accarino, Igor Ruiz Atake, Sakina-Dorothée Ayata, Amanda Calatrava, Marco Mariano De Carlo, Wout Decrop, Donatello Elia, Sandro Luigi Fiore, Marco Francescangeli, Jesús Soriano-González, Jean-Olivier Irisson, Martin Laviale, Rune Lagaisse, Antoine Lebeaud, Carolin Leluschko, Germán Moltó, Antonio Augusto Sepp Neves, Enoc Martínez, Damian Smyth, Muhammad Arabi Tayyab, Vanessa Tosello, Alvaro Lopez Garcia, Dick Schaap, Gergely Sipos, Best practices for AI-based image analysis applications in aquatic sciences: The iMagine case study, Ecological Informatics, 2025, 103306, ISSN 1574-9541, https://doi.org/10.1016/j.ecoinf.2025.103306.
Gabriele Accarino, Marco M. De Carlo, Igor Atake, Donatello Elia, Anusha L. Dissanayake, Antonio Augusto Sepp Neves, Juan Peña Ibañez, Italo Epicoco, Paola Nassisi, Sandro Fiore, Giovanni Coppini, Improving oil slick trajectory simulations with Bayesian optimization, Ecological Informatics, 2025, https://doi.org/10.1016/j.ecoinf.2025.103368.
Daniel García-Díaz, Sandra Paola Viaña-Borja, Mar Roca, Gabriel Navarro, Isabel Caballero, Blending physical and artificial intelligence models to improve satellite-derived bathymetry mapping, Ecological Informatics, Volume 90, 2025, https://doi.org/10.1016/j.ecoinf.2025.103328.
Wout Decrop, Klaas Deneudt, Parecisas Clea, Elena Schall and Elisabeth Debusschere, Transfer Learning for Distance Classification of Marine Vessels Using Underwater Sound,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, doi: 10.1109/JSTARS.2025.3593779.
2024
Aishwarya Venkataramanan, Michael Kloster, Andrea Burfeid-Castellanos, Mimoza Dani, Ntambwe A S Mayombo, Danijela Vidakovic, Daniel Langenkämper, Mingkun Tan, Cedric Pradalier, Tim Nattkemper, Martin Laviale, Bánk Beszteri, “UDE DIATOMS in the Wild 2024”: a new image dataset of freshwater diatoms for training deep learning models, GigaScience, Volume 13, 2024, giae087
Gayá-Vilar, Alberto, Alberto Abad-Uribarren, Augusto Rodríguez-Basalo, Pilar Ríos, Javier Cristobo, and Elena Prado. 2024. “Deep Learning Based Characterization of Cold-Water Coral Habitat at Central Cantabrian Natura 2000 Sites Using YOLOv8” Journal of Marine Science and Engineering 12, no. 9: 1617.
Prat, O. [et al.]. AI-based fish detection and classification at OBSEA underwater observatory. “International Conference on Marine Data and Information Systems: proceedings, volume Miscellanea INGV, 80”. Roma: Istituto Nazionale di Geofisica e Vulcanologia (INGV), 2024, p. 50-52. ISBN 2039-6651.
2023
Aishwarya Venkataramanan, Assia Benbihi, Martin Laviale, Cédric Pradalier. Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers. 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Oct 2023, Paris, France. pp.4490-4499, doi: 10.1109/ICCVW60793.2023.00483.