The morphology of isolated barchan dunes on Mars and Earth may shed light on the dynamic conditions that form them, their migration direction and the physical properties of the sediments composing them. Prior to this study, dune fields have been largely analyzed manually from aerial and satellite imagery, as automatic detection techniques are often not sufficiently accurate in outlining dunes. Here, we employ an instance segmentation neural network to detect and outline isolated barchan dunes on Mars and Earth. We train and test the model on martian targets using Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) images, and find it sufficiently accurate (mAP=77% on the test dataset) to characterize dune field dynamics. Using our trained model we detect and map the global distribution of barchan dunes relative to previously mapped dune fields, and find that barchan dunes are more abundant in the northern hemisphere than in the southern hemisphere. These contrasting abundances of barchans may reflect latitudinally dependent wind regimes, sediment supply, or sediment availability.
In the absence of consistent meteorological data on Mars, the morphology of dunes can be employed to study its atmosphere. Specifically, barchan dunes, which form under approximately unimodal winds, are reliable proxies for the dominant wind direction. Here, we characterize near-surface winds on Mars from the morphology of >106 barchans mapped globally on the planet by a convolutional neural network. Barchan migration is predominantly aligned with the global circulation: northerly at mid-latitudes and cyclonic near the north pole, with the addition of an anti-cyclonic north-polar component that likely originates from winds emerging from the ice cap. Locally, migration directions deviate from regional trends in areas with high topographic roughness. Notably, obstacles <100 km such as impact craters are efficient at deflecting surface winds. Our database, which provides insights into planetary-scale aeolian processes on modern-day Mars, can be used to constrain global circulation models to assist with predictions for future missions.
<p>The surface of Mars is riddled with dunes that form by accumulating sand particles that are carried by the wind. Since dune geometry and orientation adjust in response to prevailing wind conditions, the morphometrics of dunes can reveal information about the winds that formed them. <br><br>Previous studies inferred the prevailing local wind direction from the orientation of dunes by manually analyzing spacecraft imagery. However, building a global map remained challenging, as manual detection of individual dunes over the entire Martian surface is impractical. Here, we employ Mask R-CNN, a state-of-the-art instance segmentation neural network, to detect and analyze isolated barchan dunes on a global scale.<br><br>We prepared a training dataset by extracting Mars Context Camera (CTX) scenes of dune fields from a global CTX mosaic, as identified in the global dune-fields catalog. Images were cropped and standardized to a resolution of 832x832 pixels, and labeled using Labelbox&#8217;s online instance segmentation platform. Image augmentation and weight decay were employed to prevent overfitting during training. By inspecting 100 sample images from the validation database, we find that the network correctly identified ~86% of the isolated dunes, falsely identifying one feature as a barchan dune in a single image.</p><p>After dune outlines are detected, they are automatically analyzed to extract the dominant-wind and net sand-flux directions using traditional computer vision techniques. We expect our future surface-wind dataset to serve as a constraint for atmospheric global circulation models to help predict weather events for upcoming in situ mission as well as shed new light on the recent climate history of Mars.</p>
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