  Airports have an important role in both military and civilian domains. The
synthetic aperture radar (SAR) based airport detection has received increasing
attention in recent years. However, due to the high cost of SAR imaging and
annotation process, there is no publicly available SAR dataset for airport
detection. As a result, deep learning methods have not been fully used in
airport detection tasks. To provide a benchmark for airport detection research
in SAR images, this paper introduces a large-scale SAR Airport Dataset (SAD).
In order to adequately reflect the demands of real world applications, it
contains 624 SAR images from Sentinel 1B and covers 104 airfield instances with
different scales, orientations and shapes. The experiments of multiple deep
learning approach on this dataset proves its effectiveness. It developing
state-of-the-art airport area detection algorithms or other relevant tasks.
