  Satellite imagery solutions are widely used to study and monitor different
regions of the Earth. However, a single satellite image can cover only a
limited area. In cases where a larger area of interest is studied, several
images must be stitched together to create a single larger image, called a
mosaic, that can cover the area. Today, with the increasing number of satellite
images available for commercial use, selecting the images to build the mosaic
is challenging, especially when the user wants to optimize one or more
parameters, such as the total cost and the cloud coverage percentage in the
mosaic. More precisely, for this problem the input is an area of interest,
several satellite images intersecting the area, a list of requirements relative
to the image and the mosaic, such as cloud coverage percentage, image
resolution, and a list of objectives to optimize. We contribute to the
constraint and mixed integer lineal programming formulation of this new
problem, which we call the \textit{satellite image mosaic selection problem},
which is a multi-objective extension of the polygon cover problem. We propose a
dataset of realistic and challenging instances, where the images were captured
by the satellite constellations SPOT, Pl\'eiades and Pl\'eiades Neo. We
evaluate and compare the two proposed models and show their efficiency for
large instances, up to 200 images.
