Exploring CaSSIS with Machine Learning - The Search for Dust Devils on Mars

Abstract

Dust devils are atmospheric vortices made visible by dust entrained from the martian surface [1]. Their spatial and temporal distribution, shape, and velocity provide insights into Mars’ atmospheric dynamics, useful for, e.g., dust, weather and climate modelling [1,2]. Yet, most of the available orbital image datasets have not been thoroughly scanned for dust devils and/or are limited to images acquired at a fixed local time (sun-synchronous orbits), limiting the representativeness of current analyses. Here, we use machine learning to systematically map dust devils in the CaSSIS (Colour and Stereo Surface Imaging System) image dataset. CaSSIS is a color and stereo imaging system onboard ESA’s Trace Gas Orbiter [3] (on a non-sun-synchronous orbit) with a nominal spatial resolution of 4.6 m, covering ~6 % of Mars’ surface as of November 2022 (33,130 images).