TY - JOUR
T1 - Detection of Martian dust storms using mask regional convolutional neural networks
AU - Alshehhi, Rasha
AU - Gebhardt, Claus
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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, consisting of a regional-proposal network and a mask network. We apply the detection method to Mars daily global maps of the Mars global surveyor, Mars orbiter camera. We use center coordinates of dust storms from the eight-year Mars dust activity database 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 %. [Figure not available: see fulltext.].
AB - 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, consisting of a regional-proposal network and a mask network. We apply the detection method to Mars daily global maps of the Mars global surveyor, Mars orbiter camera. We use center coordinates of dust storms from the eight-year Mars dust activity database 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 %. [Figure not available: see fulltext.].
KW - Average precision score
KW - Dust storm
KW - Mars
KW - Mask regional convolutional neural networks
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U2 - 10.1186/s40645-021-00464-1
DO - 10.1186/s40645-021-00464-1
M3 - Article
AN - SCOPUS:85122649158
SN - 2197-4284
VL - 9
JO - Progress in Earth and Planetary Science
JF - Progress in Earth and Planetary Science
IS - 1
M1 - 4
ER -