Academic Journal
iMagine: AI-Powered Image Data Analysis in Aquatic Science
| Title: | iMagine: AI-Powered Image Data Analysis in Aquatic Science |
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| Authors: | Azmi, Elnaz, Alibabaei, Khadijeh, Kozlov, Valentin, López García, Álvaro, Schaap, Dick, Sipos, Gergely |
| Source: | Proceedings of the Platform for Advanced Scientific Computing Conference |
| Publisher Information: | ACM, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | ddc:004, Artificial intelligence, FAIR data, AI research infrastructure, DATA processing & computer science, Machine learning, Ocean science, Open science |
| Description: | The iMagine platform leverages AI-driven tools to enhance the analysis of imaging data in marine and freshwater research, contributing to the study of ocean, sea, coastal, and inland water health. Connected to the European Open Science Cloud (EOSC), it enables the development, training, and deployment of AI models, collaborating with twelve aquatic science use cases to provide valuable insights. The platform refines existing solutions from data acquisition and preprocessing to provide trained models as a service for users. iMagine outlines various AI-based tools, techniques, and methodologies for aquatic science image processing, ensuring consistency and accuracy through clear annotation guidelines and verified tools. The preparation of training datasets, along with their metadata, ensures FAIRness and effective publishing in data repositories. Deep learning models, such as Convolutional Neural Networks (CNNs), are used for classification, object detection, and segmentation, with performance metrics and evaluation tools ensuring reproducibility and transparency. AI model drift and data FAIRness are also explored, alongside case studies on AI challenges in aquatic sciences. By implementing these practices, iMagine enhances data quality, promotes reproducibility, and fosters scientific progress in aquatic research while collaborating with projects like AI4EOSC and Blue-Cloud. The platform allows users to develop, train, share, and serve AI models on its marketplace. The AI models are encapsulated as Docker images and integrated with REST APIs to ensure their reproducibility. Researchers benefit from the platform’s flexibility, which enables seamless execution of these Docker containers on both federated clouds of the European Grid Infrastructure (EGI) and High-Performance Computing (HPC) infrastructures. |
| Document Type: | Article Conference object |
| File Description: | application/pdf |
| DOI: | 10.1145/3732775.3733584 |
| DOI: | 10.5445/ir/1000182656 |
| Access URL: | https://publikationen.bibliothek.kit.edu/1000182656 https://publikationen.bibliothek.kit.edu/1000182656/167848660 https://doi.org/10.5445/IR/1000182656 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....eecd08b2c533bc0de9a45b949638671d |
| Database: | OpenAIRE |
| DOI: | 10.1145/3732775.3733584 |
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