  Wave breaking is an important process for energy dissipation in the open
ocean and coastal seas. It drives beach morphodynamics, controls air-sea
interactions, determines when ship and offshore structure operations can occur
safely, and influences on the retrieval of ocean properties from satellites.
Still, wave breaking lacks a proper physical understanding mainly due to scarce
observational field data. Consequently, new methods and data are required to
improve our current understanding of this process. In this paper we present a
novel machine learning method to detect active wave breaking, that is, waves
that are actively generating visible bubble entrainment in video imagery data.
The present method is based on classical machine learning and deep learning
techniques and is made freely available to the community alongside this
publication. The results indicate that our best performing model had a balanced
classification accuracy score of ~90% when classifying active wave breaking in
the test dataset. An example of a direct application of the method includes a
statistical description of geometrical and kinematic properties of breaking
waves. We expect that the present method and the associated dataset will be
crucial for future research related to wave breaking in several areas of
research, which include but are not limited to: improving operational forecast
models, developing risk assessment and coastal management tools, and refining
the retrieval of remotely sensed ocean properties.

Atmospheric and Oceanic Physics