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Applications of Machine Learning and AI
Advances in machine learning and artificial intelligence are enabling new ways to automate, interpret, and apply ocean observation data. Since 2014, a number of OTT projects have supported the use of neural networks to classify image data from imaging flow cytobots (IFCBs) to identify harmful algal species (HABs). These identification guide HAB forecasts and describe changes in HAB populations over time.
Another project is using Artificial Intelligence (AI) – machine learning methodologies in the operational processing scheme within the CODAR SeaSonde® high frequency (HF) radar system.The initiative seeks to implement CODAR’s new novel method of uncertainty calculations and incorporate those measurement uncertainties into SeaSonde data outputs and processing toolkit, ensuring a more nuanced representation of observed data and facilitating intelligent assimilation of CODAR data products.
The Southeast Coastal Ocean Observing Regional Association (SECOORA) and partners have successfully piloted and are working to transition to operations a coastal webcam network for the Southeast US. The network, known as WebCOOS, streamlines the collection, storage, and access to real-time and historic webcam imagery and enables image analysis and development of derived products that can be used for decision-making. These derived products, developed with open source artificial intelligence machine learning or pixel intensity thresholding algorithms, will show rip current locations, dune overwash, shoreline erosion, beach usage, and flood monitoring — all critical information needs of ocean and coastal managers.
FY24
Development & Implementation of a Low-cost Modular Ocean Discovery System
FY23
Improving HF Radar Ocean Observation with AI
A Proposal to Scale from a Regional to a National Webcam Coastal Observation System (WebCOOS)
FY20
Launching WebCOOS: Webcams for Coastal Observations and Operational Support
FY15
Transitioning IFCB for Harmful Algal Bloom Mitigation and Research
FY14
