Mathieu Lapôtre (@marslogander.bsky.social)

Wanna learn more about how to map surface winds on Mars from dunes using machine learning? Make sure to check out @Liorruba’s #vEGU21 invited presentation @ 9:05 am CEST/12:05 am PDT! Abstract:@EuroGeosciences @StanfordEarth meetingorganizer.copernicus.org/EGU21/EGU21-12…

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CO Meeting Organizer EGU21 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. 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.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’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. https://meetingorganizer.copernicus.org/EGU21/EGU21-12960.html?pdf