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Deep learning framework estimates coastal wave parameters from video

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel deep learning framework for estimating five key coastal wave parameters from monocular video. This system utilizes a V-JEPA backbone for feature extraction in challenging visual conditions, a dual-stream SlowFast temporal encoder, and an optical flow stream based on the Farneback algorithm. Despite operating in a data-limited regime with only six annotated training scenes, the framework demonstrated statistically significant temporal correlations for parameters like significant wave height and wave direction, indicating its feasibility and potential for improvement with larger datasets. AI

IMPACT This research demonstrates the potential of AI for environmental monitoring and data collection in challenging conditions.

RANK_REASON Academic paper detailing a new deep learning framework for a specific scientific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning framework estimates coastal wave parameters from video

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Academic paper detailing a new deep learning framework for a specific scientific application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Abubakar Hamisu Kamagata, Dharm Singh Jat, Attlee Munyaradzi Gamundani, Saravanakumar Paramasivam, Babangida Sani, Aliyu Zakariyya ·

    HPC-Enabled Video-based Coastal Wave Parameter Estimation Using V-JEPA and Deep Spatiotemporal Learning

    arXiv:2607.11998v1 Announce Type: cross Abstract: High deployment cost, poor spatial coverage and susceptibility to storm conditions are all challenges faced by traditional in-situ methods. This paper presents a video-based and high performance computing (HPC) enabled deep learni…

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