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CORDIS - Resultados de investigaciones de la UE
CORDIS

Imaging data and services for aquatic science

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

First Data Management Plan (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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

Best practices and guideline for developers and providers of AI-based image analytics services (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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

Policy Brief period 1 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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

Innovation Management and Exploitation Updated Plan (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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 (se abrirá en una nueva ventana)

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

Publicaciones

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

Autores: Oriol Prat, Pol Baños, Enoc Martinez, Joaquin del Rio
Publicado en: MARTECH 2024 - Mallorca (Spain), 2024

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

Autores: Pol Baños Castelló, Oriol Prat Bayarri, Enoc Martinez, Joaquin del Rio
Publicado en: MARTECH 2024 - Mallorca (Spain), 2024

iMagine: AI-Powered Image Data Analysis in Aquatic Science (se abrirá en una nueva ventana)

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

DETECT AND FOLLOW A CUSTOM OBJECT, USING OBSEA UNDERWATER CRAWLER

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

Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers (se abrirá en una nueva ventana)

Autores: Aishwarya Venkataramanan, Assia Benbihi, Martin Laviale, Cédric Pradalier
Publicado en: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Edición 96, 2024
Editor: IEEE
DOI: 10.1109/ICCVW60793.2023.00483

AI-based fish detection and classification at OBSEA underwater observatory

Autores: Oriol Prat Bayarri, Pol Baños Castelló, Enoc Martinez, Joaquin del Rio
Publicado en: International Conference on Marine Data and Information Systems - Proceedings Volume, Edición 80, 2024, ISSN 2039-6651
Editor: INGV

Automated identification of seafloor deep species (se abrirá en una nueva ventana)

Autores: Tosello Vanessa, Borremans Catherine, Lebeaud Antoine
Publicado en: 2024
Editor: Archimer
DOI: 10.13155/101744

iMagine D3.1 Technical development roadmap for the AI image analysis use cases (se abrirá en una nueva ventana)

Autores: Valentin Kozlov
Publicado en: KIT-BIBLIOTEK, 2023
Editor: KIT
DOI: 10.5281/zenodo.7760413

Blending physical and artificial intelligence models to improve satellite-derived bathymetry mapping (se abrirá en una nueva ventana)

Autores: Daniel García-Díaz, Sandra Paola Viaña-Borja, Mar Roca, Gabriel Navarro, Isabel Caballero
Publicado en: Ecological Informatics, Edición 90, 2025, ISSN 1574-9541
Editor: Elsevier BV
DOI: 10.1016/J.ECOINF.2025.103328

Improving oil slick trajectory simulations with Bayesian optimization (se abrirá en una nueva ventana)

Autores: 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
Publicado en: Ecological Informatics, Edición 91, 2025, ISSN 1574-9541
Editor: Elsevier BV
DOI: 10.1016/J.ECOINF.2025.103368

Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach (se abrirá en una nueva ventana)

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

Best practices for AI-based image analysis applications in aquatic sciences: The iMagine case study (se abrirá en una nueva ventana)

Autores: 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
Publicado en: Ecological Informatics, Edición 91, 2025, ISSN 1574-9541
Editor: Elsevier BV
DOI: 10.1016/J.ECOINF.2025.103306

Big Data Deduplication in Data Lake (se abrirá en una nueva ventana)

Autores: Jakub Hlavačka; Martin Bobák; Ladislav Hluchý
Publicado en: Acta Polytechnica Hungarica, 2024, ISSN 2064-2687
Editor: 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 (se abrirá en una nueva ventana)

Autores: Alberto Gayá-Vilar, Alberto Abad-Uribarren, Augusto Rodríguez-Basalo, Pilar Ríos, Javier Cristobo, Elena Prado
Publicado en: Journal of Marine Science and Engineering, Edición 12, 2024, ISSN 2077-1312
Editor: MDPI AG
DOI: 10.3390/jmse12091617

iMagine D4.1 Best practices and guideline for developers and providers of AI-based image analytics services (se abrirá en una nueva ventana)

Autores: Heredia, Ignacio; Kozlov, Valentin
Publicado en: KIT-BIBLIOTEK, 2024
Editor: KIT
DOI: 10.5445/IR/1000167993

iMagine D2.3 EOSC and 'AI on Demand' liaison and integration plan (se abrirá en una nueva ventana)

Autores: Gergely Sipos
Publicado en: IMAGINE, 2023
Editor: IMAGINE
DOI: 10.5281/zenodo.7793950

“UDE DIATOMS in the Wild 2024”: a new image dataset of freshwater diatoms for training deep learning models (se abrirá en una nueva ventana)

Autores: 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
Publicado en: GigaScience, Edición 13, 2024, ISSN 2047-217X
Editor: Oxford University Press (OUP)
DOI: 10.1093/GIGASCIENCE/GIAE087

Transfer Learning for Distance Classification of Marine Vessels Using Underwater Sound (se abrirá en una nueva ventana)

Autores: Wout Decrop, Klaas Deneudt, Clea Parcerisas, Elena Schall, Elisabeth Debusschere
Publicado en: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Edición 18, 2025, ISSN 1939-1404
Editor: 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 (se abrirá en una nueva ventana)

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

Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval (se abrirá en una nueva ventana)

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

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