  Remote sensing can provide crucial information for planetary rovers. However,
they must validate these orbital observations with in situ measurements.
Typically, this involves validating hyperspectral data using a spectrometer
on-board the field robot. In order to achieve this, the robot must visit
sampling locations that jointly improve a model of the environment while
satisfying sampling constraints. However, current planners follow sub-optimal
greedy strategies that are not scalable to larger regions. We demonstrate how
the problem can be effectively defined in an MDP framework and propose a
planning algorithm based on Monte Carlo Tree Search, which is devoid of the
common drawbacks of existing planners and also provides superior performance.
We evaluate our approach using hyperspectral imagery of a well-studied geologic
site in Cuprite, Nevada.
