Dept. of Geosciences Colloquium: Inferring Mars’ Wind Regime from the Global Distribution of Barchan Dunes Using Instance Segmentation Neural Networks

Lior Rubanenko, Stanford

09 November 2020, 11:00 
Zoom: https://us02web.zoom.us/j/84082397568 
Dept. of Geosciences Colloquium

Zoom: https://us02web.zoom.us/j/84082397568

 

Recording: https://us02web.zoom.us/rec/share/98SQ6BWKOl1fCl6erMPDJgXW3VAMUGttQ8JvN7JOVSibqfXgUlXitiOoXzFDPo1d.dQ4EubqhH5k566lK?startTime=1604913173000

Abstract:

The prevalence of eolian landforms on Mars is a testament to the past and present atmospheric conditions on the red planet. Deciphering the morphologic information encoded in these landforms could help characterize local and global wind circulation patterns and atmospheric history. We use an instance segmentation neural network to analyze the global distribution and morphometrics of two types of eolian features: Barchan dunes and Transverse Aeolian Ridges (TARs). Previously, the morphology of dunes mapped on Mars by orbiting spacecrafts was used to estimate past and present-day wind patterns. However, due to the great effort in extracting and analyzing individual images, these surveys were mostly focused on localized regions. Our global database includes the geographic location of individual dunes and fields of TARs, along with their orientation and typical wavelength, which are used to constrain local and global wind circulation patterns.

 

 

Event Organizers: Dr. Roy Barkan and Dr. Asaf Inbal

 

 

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