Interpreting morphological features of the seabed is a labor-intensive task for marine geologists especially when it concerns extensive portions of seabed. By applying Machine Learning (ML) techniques from the field of computer vision, it is possible to significantly streamline this process, speeding it up considerably. In this paper we present a model capable of automatically categorizing seabed features, identifying different morphological elements, such as submarine canyons, escarpments, canyon headwalls and mass movements. This model will serve as the basis for new tools to assist geologists as well as stakeholders dealing with management of coastal or offshore areas in their work, providing them with an efficient support for seabed analysis and characterization.