  We introduce a mission planning concept for routing unmanned aerial vehicles
(UAVs) through a set of sampling locations in the immediate aftermath of an
incident such as a fire or chemical accident. Using interpolation methods that
account for the spatial interdependencies inherent in the surveyed phenomenon,
these samples allow predicting the distribution of hazardous substances across
the affected area. We define the generalized correlated team orienteering
problem (GCorTOP) for selecting {informative} samples considering spatial
correlations between observed and unobserved locations as well as priorities in
the surveyed area. To quickly provide high-quality solutions in time-sensitive
situations, we propose a two-phase multi-start adaptive large neighborhood
search (2MLS). We show the competitiveness of the solution approach using
benchmark instances for the team orienteering problem and investigate the
performance of the proposed models and solution approach in an extensive study
based on newly introduced benchmark instances for the mission planning problem.
