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

By SpaceRef Editor
March 9, 2021
Filed under , , ,

Carlo Donadio, Massimo Brescia, Alessia Riccardo, Giuseppe Angora, Michele Delli Veneri, Giuseppe Riccio

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.

Comments: Accepted, To be published on Scientific Reports (Nature Research Journal), 22 pages, 3 figures, 4 tables

Subjects: Geophysics (physics.geo-ph); Earth and Planetary Astrophysics (astro-ph.EP); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (

Cite as: arXiv:2103.04116 [physics.geo-ph] (or arXiv:2103.04116v1 [physics.geo-ph] for this version)

Submission history

From: Massimo Brescia Dr

[v1] Sat, 6 Mar 2021 14:05:38 UTC (1,434 KB)

SpaceRef staff editor.