19-23 September 2022
Prague, Czech Republic
Europe/Amsterdam timezone

iMagine: Imaging data and services for aquatic science

20 Sep 2022, 19:00
1h
1st floor (Vienna Andel Prague)

1st floor

Vienna Andel Prague

Poster Machine Learning/Artificial Intelligence Posters (presenters at poster)

Speaker

Gergely Sipos (EGI.eu)

Description

iMagine is an EU-funded project providing a portfolio of image datasets, high-performance image analysis tools empowered with Artificial Intelligence (AI), and Best Practice documents for scientific image analysis ‘free at point of use’. These services and materials enable better and more efficient processing and analysis of imaging data in marine and freshwater research, accelerating our scientific insights about processes and measures relevant to healthy oceans, seas, and coastal and inland waters.
By building on the Compute platform of the European Open Science Cloud (EOSC) the project delivers a generic framework for AI model development, training, and deployment, which can be adopted by researchers for refining their AI-based applications for water pollution mitigation, biodiversity and ecosystem studies, climate change analysis and beach monitoring, but also for developing and optimising other AI-based applications in this field.
The iMagine compute layer consists of providers from the pan-European EGI federation infrastructure, collectively offering over 132,000 GPU hours, 6,000,000 CPU hours and 1500 TB-month for image hosting and processing.
The iMagine AI framework offers neural networks, parallel post-processing of very large data, and analysis of massive online data streams in distributed environments. 12 RIs will share over 9 million images and 8 AI-powered applications through the framework. Having representatives of so many RIs and IT institutes, developing a portfolio of eye-catching image processing services together will also give rise to Best Practices. The synergies between aquatic use cases will lead to common solutions in data management, quality control, performance, integration, provenance, and FAIRness, contributing to harmonisation across RIs and providing input for the iMagine Best Practice guidelines. The project results will be integrated into and will bring important contributions from RIs and e-infrastructures to EOSC and AI4EU.

Topic Machine Learning/Artificial Intelligence

Primary authors

Ilaria Fava Gergely Sipos (EGI.eu)

Presentation Materials