Comparison study on the deep-learning-based detection of Mars craters
- 1United Arab Emirates University, National Space Science and Technology Center, Al Ain, United Arab Emirates (202070624@uaeu.ac.ae)
- 2United Arab Emirates University, College of Science, Department of Physics, Al Ain, United Arab Emirates
- 3University of Toronto, Department of Physics, Toronto, Canada
Deep-learning methods are of interest for the analysis of imagery and digital elevation models from Mars orbiting satellites. They detect various atmosphere and surface characteristics. For instance, these include dust storms and craters [1,2]. We approach this topic by using the deep-learning-based crater detection algorithm DeepMars2 [3,4]. The algorithm is applied to two digital elevation models (DEMs) of the Mars surface. The DEMs are based on the satellite instruments MOLA/MGS (Mars Orbiter Laser Altimeter/Mars Global Surveyor) and HRSC/MEX (High Resolution Stereo Camera/Mars Express) and have different resolution. Crater detection statistics are compared between both DEMs.
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[2] R. Alshehhi and C. Gebhardt, "Automated Geological Landmarks Detection on Mars Using Deep Domain Adaptation From Lunar High-Resolution Satellite Images," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 2274-2283, 2022, doi: 10.1109/JSTARS.2022.3156371.
[3] Lee, C. (2019). Automated crater detection on Mars using deep learning. Planetary and Space Science, 170, 16-28. https://doi.org/10.1016/j.pss.2019.03.008
[4] Lee, C. & Hogan, J. (2021). Automated crater detection with human level performance. Computers & Geosciences, 147, 104645. https://doi.org/10.1016/j.cageo.2020.104645
How to cite: AlRiyami, H., Gebhardt, C., and Lee, C.: Comparison study on the deep-learning-based detection of Mars craters, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7761, https://doi.org/10.5194/egusphere-egu23-7761, 2023.
