Deep learning for wave height classification in satellite images for offshore wind access

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.