International Journal of Science, Engineering and TechnologyAuthors: Abhendra Pratap Singh, Nandini Sharma, Arpit Dwivedi, Vansh Garg, Saksham Aggarwal, Aakriti Sharma, Vanshika Dua
Abstract: The integration of ocean observing networks and artificial intelligence is changing how and who access marine information. This review looks at technological and recent innovations along with uses from the Argo and Biogeochemical-Argo programs, satellite remote sensing, autonomous underwater vehicles, and cloud-native data architectures. This review assesses the integration of these systems with Artificial Intelligence, particularly large language models enhanced with vector-database retrieval systems and retrieval-augmented generation (RAG). This paper discusses the major hindrances to efficient ocean data utilization like data fragmentation, also throwing light on the intricate formats like NetCDF, scalability, and policy barriers. It highlights modern practices such as practical lakehouse architectures, cloud processing, and AI systems with semantic retrieval controls. Furthermore, the review describes and discusses AI prototypes and systems built from case studies designed to assist in disaster response and mitigation, monitor climate, and manage fisheries by transforming data on spectra, profiles, and images into concise answers and easy-to-understand visual response graphics. It also talks about the persisting questions of trust, explainability, provenance, multilingual support, and inclusive engagement at the community level as the foundation of new research. These systems anticipate the provision of standard metadata, uncertainty framing, AI systems, and ethically aligned deployment pathways. Integrating resilient observing systems with human centred AI and scalable infrastructure is essential to realize a transparent, operational, and community-driven ocean-observing ecosystem promoting further research in this domain.
DOI: http://doi.org/10.5281/zenodo.21701083
