Imaging data and services for aquatic science

Projektinformationen

iMagine

ID Finanzhilfevereinbarung: 101058625

DOI

10.3030/101058625

Projekt abgeschlossen

EK-Unterschriftsdatum 26 Mai 2022

Startdatum 1 September 2022

Enddatum 31 August 2025

Finanziert unter

• Research infrastructures

Gesamtkosten

€ 4 500 000,00

EU-Beitrag

€ 4 500 000,00

Koordiniert durch

STICHTING EGI
Netherlands

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

Documents, reports (18)

First Data Management Plan

The document specifies how project publications and data will be collected, processed, monitored, catalogued, and disseminated during the project lifetime

First Innovation Management and Exploitation Plan

The document provides a definition of the innovation processes, guidelines and instruments to be used to be followed within the project. This document also specifies activities regarding Key Exploitable Results including aspects such as the definition, value proposition, IP management, exploitation path and activities and adoption.

Best practices for producers and providers of image sets and image analysis application in aquatic sciences

Based on the lessons/experiences gained during the development of image improvement, alignment and AI analysis applications.

Technical development roadmap updated for the mature AI image analysis use cases

Technical plan for the development, enhancements, improvement, integration activities for the 5 mature image AI use cases, and user requirements for the generic iMagine AI framework

Policy Brief period 2

"The updated version of D1.5 ""Policy Brief period 1"""

Best practices and guideline for developers and providers of AI-based image analytics services

Good practices, tips, guidance and stage acceptance tests for those who wish to develop and operate AI-based image analytics services with the iMagine AI services.

1st periodical assessment of Imaging VA services

The report provides assessment and statistics of all the Imaging data and analysis tools services provided under virtual acces

Technical development roadmap for the mature AI image analysis use cases

Technical plan for the development, enhancements, improvement, integration activities for the 5 mature image AI use cases, and user requirements for the generic iMagine AI framework

Communication, Dissemination and Engagement Updated plan

This document provides an update on the communication, dissemination and engagement activities and plans as defined in D3.1.

AI application upgrade/deployment, and operation plan

Plan about the application deployment and operation processes required to deliver the WP3 service outcomes within the WP5 service environments.

EOSC and AI4EU liaison and integration plan

A FAIR-ness assessment and improvement plan for the iMagine services, and a plan for liaison with and integrating services and activities between iMagine and EOSC and the various AI4EU entities (legal entities, WGs, AGs, projects, etc.

2nd Periodical assessment of AI and Infrastructure services

The report provides assessment and statistics of all the AI and Infrastructure services provided under virtual access

Policy Brief period 1

The document addresses policy and practice with evidence-based recommendations derived from the project's activities and results.

1st Periodical assessment of AI and Infrastructure services

The report provides assessment and statistics of all the AI and Infrastructure services provided under virtual access

Innovation Management and Exploitation Updated Plan

The document provides an update of the innovation processes, guidelines and instruments along with the exploitation activities and plans as defined in D3.2. This report also includes an initial business model analysis and sustainability plan.

Business Model analysis and Sustainability Plan

Provides an analysis on the current status of the market and will also identify alternative business models for sustainability of each thematic services

First Communication, Dissemination and Engagement plan

The document will provide an in-depth description of how project results, developments and branding will be communicated as well as engagement with the targeted audiences, a clear dissemination strategy, and the description of promotion, consultancy, outreach, training and co-design activities. The document will also provide a dissemination plan.

2nd periodical assessment of Imaging VA services

The report provides assessment and statistics of all the Imaging data and analysis tools services provided under virtual access.

Veröffentlichungen

Peer reviewed articles (13)

iMagine: Revolutionising Aquatic Sciences with AI-Driven Image Analysis

Autoren: Sipos, Gergely, Schaap, Dick
Veröffentlicht in: ERCIM News, Ausgabe 140, 2025, ISSN 1564-0094
Herausgeber: ERCIM
DOI: 10.5281/zenodo.16963449

iMagine D3.1 Technical development roadmap for the AI image analysis use cases

Autoren: Valentin Kozlov
Veröffentlicht in: KIT-BIBLIOTEK, 2023
Herausgeber: KIT
DOI: 10.5281/zenodo.7760413

Blending physical and artificial intelligence models to improve satellite-derived bathymetry mapping

Autoren: Daniel García-Díaz, Sandra Paola Viaña-Borja, Mar Roca, Gabriel Navarro, Isabel Caballero
Veröffentlicht in: Ecological Informatics, Ausgabe 90, 2025, ISSN 1574-9541
Herausgeber: Elsevier BV
DOI: 10.1016/J.ECOINF.2025.103328

Improving oil slick trajectory simulations with Bayesian optimization

Autoren: Gabriele Accarino, Marco M. De Carlo, Igor Ruiz Atake, Donatello Elia, Anusha L. Dissanayake, Antonio Augusto Sepp Neves, Juan Peña Ibañez, Italo Epicoco, Paola Nassisi, Sandro Fiore, Giovanni Coppini
Veröffentlicht in: Ecological Informatics, Ausgabe 91, 2025, ISSN 1574-9541
Herausgeber: Elsevier BV
DOI: 10.1016/J.ECOINF.2025.103368

Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach

Autoren: Aishwarya Venkataramanan, Pierre Faure-Giovagnoli, Cyril Regan, David Heudre, Cécile Figus, Philippe Usseglio-Polatera, Cédric Pradalier, Martin Laviale
Veröffentlicht in: Engineering Applications of Artificial Intelligence, Ausgabe 117, 2024, ISSN 0952-1976
Herausgeber: Elsevier BV
DOI: 10.1016/j.engappai.2022.105594

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

Autoren: 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
Veröffentlicht in: Ecological Informatics, Ausgabe 91, 2025, ISSN 1574-9541
Herausgeber: Elsevier BV
DOI: 10.1016/J.ECOINF.2025.103306

Big Data Deduplication in Data Lake

Autoren: Jakub Hlavačka; Martin Bobák; Ladislav Hluchý
Veröffentlicht in: Acta Polytechnica Hungarica, 2024, ISSN 2064-2687
Herausgeber: Budapest Óbuda University
DOI: 10.12700/APH.21.11.2024.11.17

Deep Learning Based Characterization of Cold-Water Coral Habitat at Central Cantabrian Natura 2000 Sites Using YOLOv8

Autoren: Alberto Gayá-Vilar, Alberto Abad-Uribarren, Augusto Rodríguez-Basalo, Pilar Ríos, Javier Cristobo, Elena Prado
Veröffentlicht in: Journal of Marine Science and Engineering, Ausgabe 12, 2024, ISSN 2077-1312
Herausgeber: MDPI AG
DOI: 10.3390/jmse12091617

iMagine D4.1 Best practices and guideline for developers and providers of AI-based image analytics services

Autoren: Heredia, Ignacio; Kozlov, Valentin
Veröffentlicht in: KIT-BIBLIOTEK, 2024
Herausgeber: KIT
DOI: 10.5445/IR/1000167993

iMagine D2.3 EOSC and 'AI on Demand' liaison and integration plan

Autoren: Gergely Sipos
Veröffentlicht in: IMAGINE, 2023
Herausgeber: IMAGINE
DOI: 10.5281/zenodo.7793950

“UDE DIATOMS in the Wild 2024”: a new image dataset of freshwater diatoms for training deep learning models

Autoren: 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
Veröffentlicht in: GigaScience, Ausgabe 13, 2024, ISSN 2047-217X
Herausgeber: Oxford University Press (OUP)
DOI: 10.1093/GIGASCIENCE/GIAE087

Transfer Learning for Distance Classification of Marine Vessels Using Underwater Sound

Autoren: Wout Decrop, Klaas Deneudt, Clea Parcerisas, Elena Schall, Elisabeth Debusschere
Veröffentlicht in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Ausgabe 18, 2025, ISSN 1939-1404
Herausgeber: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/JSTARS.2025.3593779

Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers

Autoren: Venkataramanan, Aishwarya; Benbihi, Assia; Laviale, Martin; Pradalier, Cédric
Veröffentlicht in: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023, ISSN 2473-9944
Herausgeber: IEEE
DOI: 10.48550/ARXIV.2305.13849

Conference proceedings (7)

TOOLS FOR ECOSYSTEM MONITORING BASED ON FISH DETECTION AND CLASSIFICATION USING DEEP NEURAL NETWORKS

Autoren: Oriol Prat, Pol Baños, Enoc Martinez, Joaquin del Rio
Veröffentlicht in: MARTECH 2024 - Mallorca (Spain), 2024

EVALUATING THE BIOLOGICAL IMPACT OF AN ARTIFICIAL REEF USING DEEP LEARNING TECHNIQUES

Autoren: Pol Baños Castelló, Oriol Prat Bayarri, Enoc Martinez, Joaquin del Rio
Veröffentlicht in: MARTECH 2024 - Mallorca (Spain), 2024

iMagine: AI-Powered Image Data Analysis in Aquatic Science

Autoren: Elnaz Azmi; Khadijeh Alibabaei; Valentin Kozlov; Álvaro López García; Dick Schaap; Gergely Sipos
Veröffentlicht in: Proceedings of the Platform for Advanced Scientific Computing Conference, 2025, ISBN 9798400718861
Herausgeber: ACM
DOI: 10.5445/IR/1000182656

DETECT AND FOLLOW A CUSTOM OBJECT, USING OBSEA UNDERWATER CRAWLER

Autoren: Ahmad Falahzadeh, Daniel Mihai Toma, Marc Nogueras, Enoc Martines, Matias Carandell, Jacopo Aguzzi and Joaquín del Río
Veröffentlicht in: MARTECH 2024 - Mallorca (Spain), 2024

Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers

Autoren: Aishwarya Venkataramanan, Assia Benbihi, Martin Laviale, Cédric Pradalier
Veröffentlicht in: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Ausgabe 96, 2024
Herausgeber: IEEE
DOI: 10.1109/ICCVW60793.2023.00483

AI-based fish detection and classification at OBSEA underwater observatory

Autoren: Oriol Prat Bayarri, Pol Baños Castelló, Enoc Martinez, Joaquin del Rio
Veröffentlicht in: International Conference on Marine Data and Information Systems - Proceedings Volume, Ausgabe 80, 2024, ISSN 2039-6651
Herausgeber: INGV

Automated identification of seafloor deep species

Autoren: Tosello Vanessa, Borremans Catherine, Lebeaud Antoine
Veröffentlicht in: 2024
Herausgeber: Archimer
DOI: 10.13155/101744

Book chapters (1)

Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval

Autoren: Aishwarya Venkataramanan, Martin Laviale, Cédric Pradalier
Veröffentlicht in: Lecture Notes in Computer Science, Computer Vision Systems, 2023
Herausgeber: Springer Nature Switzerland
DOI: 10.1007/978-3-031-44137-0_35