  Along with the increasing use of unmanned aerial vehicles (UAVs), large
volumes of aerial videos have been produced. It is unrealistic for humans to
screen such big data and understand their contents. Hence methodological
research on the automatic understanding of UAV videos is of paramount
importance. In this paper, we introduce a novel problem of event recognition in
unconstrained aerial videos in the remote sensing community and present a
large-scale, human-annotated dataset, named ERA (Event Recognition in Aerial
videos), consisting of 2,864 videos each with a label from 25 different classes
corresponding to an event unfolding 5 seconds. The ERA dataset is designed to
have a significant intra-class variation and inter-class similarity and
captures dynamic events in various circumstances and at dramatically various
scales. Moreover, to offer a benchmark for this task, we extensively validate
existing deep networks. We expect that the ERA dataset will facilitate further
progress in automatic aerial video comprehension. The website is
https://lcmou.github.io/ERA_Dataset/
