32 citations · 47 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 15 cited
Using Machine Learning to Reduce Observational Biases When Detecting New Impacts on Mars
Kiri L. Wagstaff, Ingrid J. Daubar, Gary Doran +5
The current inventory of recent (fresh) impacts on Mars shows a strong bias towards areas of low thermal inertia. These areas are generally visually bright, and impacts create dark…
astro-ph.IM2020★ 32 cited
Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade
Abigail R. Azari, John B. Biersteker, Ryan M. Dewey +13
Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen…