Imaging data and services for aquatic science

Informazioni relative al progetto

iMagine

ID dell’accordo di sovvenzione: 101058625

DOI

10.3030/101058625

Progetto chiuso

Data della firma CE 26 Maggio 2022

Data di avvio 1 Settembre 2022

Data di completamento 31 Agosto 2025

Finanziato da

• Research infrastructures

Costo totale

€ 4 500 000,00

Contributo UE

€ 4 500 000,00

Coordinato da

STICHTING EGI
Netherlands

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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.

Pubblicazioni

Conference proceedings (6)

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

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

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

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

DETECT AND FOLLOW A CUSTOM OBJECT, USING OBSEA UNDERWATER CRAWLER

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

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

Autori: Aishwarya Venkataramanan, Assia Benbihi, Martin Laviale, Cédric Pradalier
Pubblicato in: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Numero 96, 2024
Editore: IEEE
DOI: 10.1109/ICCVW60793.2023.00483

AI-based fish detection and classification at OBSEA underwater observatory

Autori: Oriol Prat Bayarri, Pol Baños Castelló, Enoc Martinez, Joaquin del Rio
Pubblicato in: IMDIS 2024 - Bergen (Norway), 2024

Automated identification of seafloor deep species

Autori: Tosello Vanessa, Borremans Catherine, Lebeaud Antoine
Pubblicato in: 2024
Editore: Archimer
DOI: 10.13155/101744

Peer reviewed articles (5)

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

Autori: Valentin Kozlov
Pubblicato in: KIT-BIBLIOTEK, 2023
Editore: KIT
DOI: 10.5281/zenodo.7760413

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

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

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

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

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

Autori: Heredia, Ignacio; Kozlov, Valentin
Pubblicato in: KIT-BIBLIOTEK, 2024
Editore: KIT
DOI: 10.5445/IR/1000167993

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

Autori: Gergely Sipos
Pubblicato in: IMAGINE, 2023
Editore: IMAGINE
DOI: 10.5281/zenodo.7793950

Book chapters (1)

Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval

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