  Several approaches were proposed to describe the geomorphology of drainage
networks and the abiotic/biotic factors determining their morphology. There is
an intrinsic complexity of the explicit qualification of the morphological
variations in response to various types of control factors and the difficulty
of expressing the cause-effect links. Traditional methods of drainage network
classification are based on the manual extraction of key characteristics, then
applied as pattern recognition schemes. These approaches, however, have low
predictive and uniform ability. We present a different approach, based on the
data-driven supervised learning by images, extended also to extraterrestrial
cases. With deep learning models, the extraction and classification phase is
integrated within a more objective, analytical, and automatic framework.
Despite the initial difficulties, due to the small number of training images
available, and the similarity between the different shapes of the drainage
samples, we obtained successful results, concluding that deep learning is a
valid way for data exploration in geomorphology and related fields.
