Detection of Martian Dust Storms Using Mask-Regional Convolutional Neural Networks

Abstract

\n Martian dust plays a crucial role in the meteorology and climate of the Martian atmosphere. It heats the atmosphere, enhances the atmospheric general circulation, and affects spacecraft instruments and operations. Compliant with that, studying dust is also essential for future human exploration. In this work, we present a method for the deep-learning-based detection of the areal extent of dust storms in Mars satellite imagery. We use a mask regional convolutional neural network (R-CNN), consisting of a regional-proposal network (RPN) and a mask network. We apply the detection method to Mars Daily Global Maps (MDGMs) of the Mars Global Surveyor (MGS) Mars Orbiter Camera (MOC). We use center coordinates of dust storms from the eight-year Mars Dust Activity Database (MDAD) as ground-truth to train and validate the method. The performance of the regional network is evaluated by the average precision score with 50% overlap (mAP50), which is around 62.1%.