Detecting and measuring Transverse Aeolian Ridges on Mars using deep learning

In this investigation, a Deep Learning (DL) approach was applied to measure the morphometry of Transverse Aeolian Ridges (TARs) on the surface of Mars. A large sample of TARs was segmented from High Resolution Imaging Science Experiment (HiRISE) images, the highest resolution remote sensing dataset presently available for the planet. HiRISE images located between 50°N and 50°S, and from all longitudes were selected. Morphometric parameters such as area, elongation, and orientation were retrieved for this sample using a supervised instance segmentation and geospatial analysis pipeline. The result is the most extensive catalogue of TAR morphometry to date extracted from ~14 million candidate TARs in ~7000 HiRISE Images. This was accomplished by training off-the-shelf DL models within a Geographic Information System (GIS) environment. A significant TAR population was found in approximately half of the images surveyed. TAR area, and the lengths of the long and short axes, were found to exhibit a positively skewed log-normal distribution; the median short axis length is 5 m, while the median long axis is 24 m. Median elongation is 0.24. Global TAR orientations are varied, although North-South oriented TARs are the most populous group. This is likely due to the strong east blowing winds predicted by GCM simulations of the modern martian climate. Here we present our latest results and use TAR orientation statistics to describe the emerging picture of global wind patterns on Mars during TAR forming epochs. • TARs have been segmented with a Mask R-CNN model, using ArcGIS Pro. • 14 million TARs were measured in ~7000 HiRISE images of the mid latitudes of Mars. • Orientation and morphometry data were retrieved using an automated DL/GIS pipeline. • TAR orientation provides a geomorphic markers for past wind conditions on Mars. • Comparisons to manual digitisations show the results to be representative and fit for purpose.