Detection and tracking of barchan dunes using Artificial Intelligence
Esteban Andr\'es C\'u\~nez Benalc\'azar, Erick de Moraes Franklin

TL;DR
This paper demonstrates that a neural network can effectively detect and track interacting barchan dunes in satellite images from Earth and Mars, surpassing previous isolated detection methods and enabling better monitoring of these bedforms.
Contribution
It introduces a neural network trained on complex dune interactions, capable of identifying and tracking barchans in diverse environments and image types with high confidence.
Findings
Neural network achieved over 70% accuracy in detecting interacting barchans.
The method works across different planets, environments, and image conditions.
First demonstration of AI tracking of interacting dunes in satellite imagery.
Abstract
Barchans are crescent-shape dunes ubiquitous on Earth and other celestial bodies, which are organized in barchan fields where they interact with each other. Over the last decades, satellite images have been largely employed to detect barchans on Earth and on the surface of Mars, with AI (Artificial Intelligence) becoming an important tool for monitoring those bedforms. However, automatic detection reported in previous works is limited to isolated dunes and does not identify successfully groups of interacting barchans. In this paper, we inquire into the automatic detection and tracking of barchans by carrying out experiments and exploring the acquired images using AI. After training a neural network with images from controlled experiments where complex interactions took place between dunes, we did the same for satellite images from Earth and Mars. We show, for the first time, that a…
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