Automatic detection of barchan dunes on Mars employing an instance segmentation neural network
The surface of Mars is riddled with dunes created by accumulating sand particles that are carried by the wind. When the sand supply is limited and the wind is approximately unidirectional, dunes take the form of crescents termed barchan dunes, whose slip faces are oriented in the dominant wind direction. Consequently, analyzing the morphometrics of barchan dunes can help characterize the winds that form them. Previously, local circulation patterns were derived by analyzing individual images of barchan dunes near the North Pole of Mars . However, repeating this analysis on a global scale remains a challenge, as manually mapping dunes is largely impractical and traditional computer vision algorithms are largely ineffective at identifying the outlines of dunes from images. Here we employ Mask R-CNN , an instance segmentation convolutional neural network, to map dunes across the surface of Mars. Training on ~1000 images, our model achieves a mean average detection precision (mAP) of 80%, for IoU = 0.5. In the talk, I will describe the Mask R-CNN neural network and its vast space of hyperparameters, and how those can be employed for object detection and analysis by incorporating traditional computer vision techniques.