  The increasing availability of satellite and aerial imagery has sparked
substantial interest in automatically updating street maps by processing aerial
images. Until now, the community has largely focused on road extraction, where
road networks are inferred from scratch from an aerial image. However, given
that relatively high-quality maps exist in most parts of the world, in
practice, inference approaches must be applied to update existing maps rather
than infer new ones. With recent road extraction methods showing high accuracy,
we argue that it is time to transition to the more practical map update task,
where an existing map is updated by adding, removing, and shifting roads,
without introducing errors in parts of the existing map that remain up-to-date.
In this paper, we develop a new dataset called MUNO21 for the map update task,
and show that it poses several new and interesting research challenges. We
evaluate several state-of-the-art road extraction methods on MUNO21, and find
that substantial further improvements in accuracy will be needed to realize
automatic map update.
