OceanAI: A Conversational Platform for Accurate, Transparent, Near-Real-Time Oceanographic Insights

• URL: http://arxiv.org/abs/2511.01019v2
• Date: Thu, 06 Nov 2025 16:53:45 GMT
• Title: OceanAI: A Conversational Platform for Accurate, Transparent, Near-Real-Time Oceanographic Insights
• Authors: Bowen Chen, Jayesh Gajbhar, Gregory Dusek, Rob Redmon, Patrick Hogan, Paul Liu, DelWayne Bohnenstiehl, Dongkuan Xu, Ruoying He,
• Abstract summary: We present OceanAI, a conversational platform that integrates the natural-language fluency of open-source large language models.<n>Each query triggers real-time API calls that identify, parse, and synthesize relevant datasets.<n>OceanAI connects to multiple NOAA data products and variables, supporting applications in marine hazard forecasting, ecosystem assessment, and water-quality monitoring.
• Score: 17.632037709212266
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence is transforming the sciences, yet general conversational AI systems often generate unverified "hallucinations" undermining scientific rigor. We present OceanAI, a conversational platform that integrates the natural-language fluency of open-source large language models (LLMs) with real-time, parameterized access to authoritative oceanographic data streams hosted by the National Oceanic and Atmospheric Administration (NOAA). Each query such as "What was Boston Harbor's highest water level in 2024?" triggers real-time API calls that identify, parse, and synthesize relevant datasets into reproducible natural-language responses and data visualizations. In a blind comparison with three widely used AI chat-interface products, only OceanAI produced NOAA-sourced values with original data references; others either declined to answer or provided unsupported results. Designed for extensibility, OceanAI connects to multiple NOAA data products and variables, supporting applications in marine hazard forecasting, ecosystem assessment, and water-quality monitoring. By grounding outputs and verifiable observations, OceanAI advances transparency, reproducibility, and trust, offering a scalable framework for AI-enabled decision support within the oceans. A public demonstration is available at https://oceanai.ai4ocean.xyz.

Related papers

• Sensorium Arc: AI Agent System for Oceanic Data Exploration and Interactive Eco-Art [3.0447481187978886]
  Sensorium Arc (AI reflects on climate) is a real-time multimodal interactive AI agent system that personifies the ocean as a poetic speaker.<n>The project demonstrates the potential of conversational AI agents to mediate affective, intuitive access to high-dimensional environmental data.
  arXiv  Detail & Related papers  (2025-11-20T02:48:40Z)
• Advancing Ocean State Estimation with efficient and scalable AI [22.24444646069193]
  We present an AI-driven Data Assimilation Framework for Ocean (ADAF-Ocean) that assimilates multi-source and multi-scale data.<n>ADAF-Ocean learns a continuous mapping from heterogeneous inputs to ocean states, preserving native data fidelity.
  arXiv  Detail & Related papers  (2025-11-08T15:24:23Z)
• OceanGym: A Benchmark Environment for Underwater Embodied Agents [69.56465775825275]
  OceanGym is the first comprehensive benchmark for ocean underwater embodied agents.<n>It is designed to advance AI in one of the most demanding real-world environments.<n>By providing a high-fidelity, rigorously designed platform, OceanGym establishes a testbed for developing robust embodied AI.
  arXiv  Detail & Related papers  (2025-09-30T17:09:32Z)
• A Sentinel-3 foundation model for ocean colour [8.571925606193703]
  We describe a new foundation model using the Prithvi-EO Vision Transformer architecture which has been pre-trained to reconstruct data from the Sentinel-3 Ocean and Land Colour Instrument (OLCI)<n>We evaluate the model by fine-tuning on two downstream marine earth observation tasks.<n>We conclude that this new generation of geospatial AI models has the potential to provide more robust, data-driven insights into ocean ecosystems and their role in global climate processes.
  arXiv  Detail & Related papers  (2025-09-25T15:00:38Z)
• Neptune-X: Active X-to-Maritime Generation for Universal Maritime Object Detection [54.1960918379255]
  Neptune-X is a data-centric generative-selection framework for maritime object detection.<n>X-to-Maritime is a multi-modality-conditioned generative model that synthesizes diverse and realistic maritime scenes.<n>Our approach sets a new benchmark in maritime scene synthesis, significantly improving detection accuracy.
  arXiv  Detail & Related papers  (2025-09-25T04:59:02Z)
• LangYa: Revolutionizing Cross-Spatiotemporal Ocean Forecasting [17.125674052741928]
  We introduce LangYa, a cross-spatiotemporal and air-sea coupled ocean forecasting system.<n>Results demonstrate that LangYa enables a single model to make forecasts with lead times ranging from 1 to 7 days.<n>Compared to existing numerical and AI-based ocean forecasting systems, LangYa uses 27 years of global ocean data from the Global Ocean Reanalysis and Simulation version 12.
  arXiv  Detail & Related papers  (2024-12-24T02:14:39Z)
• Where are we in audio deepfake detection? A systematic analysis over generative and detection models [59.09338266364506]
  SONAR is a synthetic AI-Audio Detection Framework and Benchmark.<n>It provides a comprehensive evaluation for distinguishing cutting-edge AI-synthesized auditory content.<n>It is the first framework to uniformly benchmark AI-audio detection across both traditional and foundation model-based detection systems.
  arXiv  Detail & Related papers  (2024-10-06T01:03:42Z)
• Introducing VaDA: Novel Image Segmentation Model for Maritime Object Segmentation Using New Dataset [3.468621550644668]
  The maritime shipping industry is undergoing rapid evolution driven by advancements in computer vision artificial intelligence (AI) object recognition in maritime environments faces challenges such as light reflection, interference, intense lighting, and various weather conditions. Existing AI recognition models and datasets have limited suitability for composing autonomous navigation systems.
  arXiv  Detail & Related papers  (2024-07-12T05:48:53Z)
• Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
  We present an AI-based data assimilation model, i.e., Adas, for global weather variables. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
  arXiv  Detail & Related papers  (2023-12-18T09:05:28Z)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover Classification [63.072664304695465]
  In this letter, four data fusion paradigms, based on Convolutional Neural Networks (CNNs) are analyzed and implemented. The goals are to provide a systematic procedure for choosing the best data fusion framework, resulting in the best classification results. The procedure has been validated for land-cover classification but it can be transferred to other cases.
  arXiv  Detail & Related papers  (2021-06-18T11:36:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.