  Seasonal frosting and defrosting on the surface of Mars is hypothesized to
drive both climate processes and the formation and evolution of
geomorphological features such as gullies. Past studies have focused on
manually analyzing the behavior of the frost cycle in the northern mid-latitude
region of Mars using high-resolution visible observations from orbit. Extending
these studies globally requires automating the detection of frost using data
science techniques such as convolutional neural networks. However, visible
indications of frost presence can vary significantly depending on the geologic
context on which the frost is superimposed. In this study, we (1) present a
novel approach for spatially partitioning data to reduce biases in model
performance estimation, (2) illustrate how geologic context affects automated
frost detection, and (3) propose mitigations to observed biases in automated
frost detection.
