  Aerial scene classification, which aims to automatically label an aerial
image with a specific semantic category, is a fundamental problem for
understanding high-resolution remote sensing imagery. In recent years, it has
become an active task in remote sensing area and numerous algorithms have been
proposed for this task, including many machine learning and data-driven
approaches. However, the existing datasets for aerial scene classification like
UC-Merced dataset and WHU-RS19 are with relatively small sizes, and the results
on them are already saturated. This largely limits the development of scene
classification algorithms. This paper describes the Aerial Image Dataset (AID):
a large-scale dataset for aerial scene classification. The goal of AID is to
advance the state-of-the-arts in scene classification of remote sensing images.
For creating AID, we collect and annotate more than ten thousands aerial scene
images. In addition, a comprehensive review of the existing aerial scene
classification techniques as well as recent widely-used deep learning methods
is given. Finally, we provide a performance analysis of typical aerial scene
classification and deep learning approaches on AID, which can be served as the
baseline results on this benchmark.
