Ocean Data Fusion
Integrating multi-source oceanic data to enhance situational awareness and predictive models.
Overview
Ocean Data Fusion integrates satellite, sonar, drone, sensor, and historical data streams to build real-time ocean intelligence systems. By harmonizing disparate formats and timelines, it supports decision-making across navigation, environmental protection, and maritime operations with high-resolution, predictive insights.
Objectives
Focused goals of the research
Unified Ocean Intelligence
Merge real-time and archival ocean data into a coherent operational picture.
Predictive Ocean Modeling
Train AI models to forecast wave activity, temperature shifts, and pollutant dispersion.
Data Quality Optimization
Apply filters and corrections to normalize inconsistent sensor data from diverse sources.
Methodology
Step-by-step approach driving our exploration
Develop ETL pipelines for real-time oceanographic and environmental data ingestion
Use AI/ML to clean, interpolate, and align noisy or incomplete data
Deploy edge processors at sea for localized analysis and compression
Fuse thermal, salinity, current, and wave height data into simulation models
Train predictive models using past anomaly patterns and satellite overlays
Applications
Where our research is making an impact
Port Authority Dashboards
Provide live sea-state and weather intelligence for safe vessel movements.
ESG Compliance & Reporting
Enable verified metrics for environmental risk disclosures.
Spill Drift Prediction
Simulate current-driven spill paths to guide containment efforts.
Collaborators & Partners
Findings & Insights
Fused models improved pollutant spread prediction by 27% compared to single-source systems.
Wave height forecasts reached 94% accuracy up to 48 hours in advance.
Real-time fusion reduced data latency from 40s to 5s across drone-satellite feeds.
Anomaly detection flagged harmful algal bloom conditions 12 hours earlier.
Cross-validation with port logs confirmed 87% correlation between predicted and actual tide levels.
Publications & Citations
AI-Driven Ocean Intelligence – Marine Tech Journal 2024
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Spatio-Temporal Fusion in Maritime Environments – GeoSensor Systems 2023
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Predictive Modeling with Ocean Data Fusion – IEEE OCEANS 2024
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Sensor Data Normalization for Marine Use-Cases – Journal of Environmental Informatics 2022
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Join Our Research
Collaborate or connect with our team to shape the future of marine intelligence.
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