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Several approaches were proposed to describe the geomorphology of drainagenetworks and the abiotic/biotic factors determining their morphology. There isan intrinsic complexity of the explicit qualification of the morphologicalvariations in response to various types of control factors and the difficultyof expressing the cause-effect links. Traditional methods of drainage networkclassification are based on the manual extraction of key characteristics, thenapplied as pattern recognition schemes. These approaches, however, have lowpredictive and uniform ability. We present a different approach, based on thedata-driven supervised learning by images, extended also to extraterrestrialcases. With deep learning models, the extraction and classification phase isintegrated within a more objective, analytical, and automatic framework.Despite the initial difficulties, due to the small number of training imagesavailable, and the similarity between the different shapes of the drainagesamples, we obtained successful results, concluding that deep learning is avalid way for data exploration in geomorphology and related fields.

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on Solar System bodies