arXiv:2606.13302v1 Announce Type: new Abstract: Wave parameters in the nearshore are crucial for coastal engineering, shoreline protection, marine hazard assessment, and coastal management for climate resilience. Traditional monitoring systems like buoys and radar platforms offer accurate monitoring but can have high installation and maintenance expenses and limited spatial coverage. Passive ocean monitoring using video has been achieved by leveraging deep learning, however, many methods are not physically interpretable, feasible, and validated for oceanography. In thiswork, a Physics-Guided D
The increasing availability of high-resolution video data and advancements in AI, particularly physics-guided deep learning, enable more sophisticated and accurate environmental monitoring solutions that were previously less feasible.
This research outlines a method to estimate critical coastal wave parameters more affordably and widely, enabling better coastal management and climate resilience efforts with reduced reliance on expensive traditional infrastructure.
The feasibility of cost-effective, real-time, and scalable monitoring of coastal conditions through passive video analysis is enhanced, potentially democratizing access to crucial oceanographic data.
- · Coastal engineering firms
- · Environmental monitoring technology providers
- · Coastal communities
- · Climate resilience initiatives
- · Providers of traditional, high-cost buoy/radar systems
- · Organizations reliant solely on sparse data for coastal analysis
Improved accuracy and accessibility of coastal wave data for engineering and climate adaptation.
Reduced infrastructure costs for marine hazard assessment and enhanced shoreline protection strategies.
More informed policy-making for coastal zones, potentially leading to advanced climate resilience planning and infrastructure development.
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Read at arXiv cs.AI