  This research proposes a new integrated framework for identifying safe
landing locations and planning in-flight divert maneuvers. The state-of-the-art
algorithms for landing zone selection utilize local terrain features such as
slopes and roughness to judge the safety and priority of the landing point.
However, when there are additional chances of observation and diverting in the
future, these algorithms are not able to evaluate the safety of the decision
itself to target the selected landing point considering the overall descent
trajectory. In response to this challenge, we propose a reinforcement learning
framework that optimizes a landing site selection strategy concurrently with a
guidance and control strategy to the target landing site. The trained agent
could evaluate and select landing sites with explicit consideration of the
terrain features, quality of future observations, and control to achieve a safe
and efficient landing trajectory at a system-level. The proposed framework was
able to achieve 94.8 $\%$ of successful landing in highly challenging landing
sites where over 80$\%$ of the area around the initial target lading point is
hazardous, by effectively updating the target landing site and feedback control
gain during descent.
