Abstract
Measuring wave heights has traditionally been associated with physical buoy tools that aim to measure and average multiple wave heights over a period of time. With our method, we demonstrate a process of utilizing large-scale satellite images to classify a wave height with a continuous regressive output using a corresponding input for close shore sea. We generated and trained a convolutional neural network model that achieved an average loss of 0.17 m (Fig. 8). Providing an inexpensive and scalable approach for uses in multiple sectors, with practical applications for offshore wind farms.
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Spick, R.J., Walker, J.A. (2018). Deep Learning for Wave Height Classification in Satellite Images for Offshore Wind Access. In: Woon, W., Aung, Z., Catalina Feliú, A., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. Technologies, Systems and Society. DARE 2018. Lecture Notes in Computer Science(), vol 11325. Springer, Cham. https://doi.org/10.1007/978-3-030-04303-2_6
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DOI: https://doi.org/10.1007/978-3-030-04303-2_6
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