Using machine learning to reduce observational biases when detecting new impacts on Mars

Author:

Wagstaff Kiri L.ORCID,Daubar Ingrid J.,Doran Gary,Munje Michael J.,Bickel Valentin T.,Gao Annabelle,Pate Joe,Wexler Daniel

Funder

Deutscher Akademischer Austauschdienst

NASA

Publisher

Elsevier BV

Subject

Space and Planetary Science,Astronomy and Astrophysics

Reference48 articles.

1. Alexandari, A., Kundaje, A., Shrikumar, A., 2020. Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation. In: Proceedings of the 2020 International Conference on Machine Learning, pp. 222–232.

2. NOAH-H, a deep-learning, terrain classification system for mars: Results for the ExoMars rover candidate landing sites;Barrett;Icarus,2022

3. Dark halos produced by current impact cratering on Mars;Bart;Icarus,2019

4. Impacts drive lunar rockfalls over billions of years;Bickel;Nature Commun.,2020

5. Deep learning-driven detection and mapping of rockfalls on Mars;Bickel;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2020

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