  To certify UAV operations in populated areas, risk mitigation strategies --
such as Emergency Landing (EL) -- must be in place to account for potential
failures. EL aims at reducing ground risk by finding safe landing areas using
on-board sensors. The first contribution of this paper is to present a new EL
approach, in line with safety requirements introduced in recent research. In
particular, the proposed EL pipeline includes mechanisms to monitor learning
based components during execution. This way, another contribution is to study
the behavior of Machine Learning Runtime Monitoring (MLRM) approaches within
the context of a real-world critical system. A new evaluation methodology is
introduced, and applied to assess the practical safety benefits of three MLRM
mechanisms. The proposed approach is compared to a default mitigation strategy
(open a parachute when a failure is detected), and appears to be much safer.
