  In the past decade, object detection has achieved significant progress in
natural images but not in aerial images, due to the massive variations in the
scale and orientation of objects caused by the bird's-eye view of aerial
images. More importantly, the lack of large-scale benchmarks becomes a major
obstacle to the development of object detection in aerial images (ODAI). In
this paper, we present a large-scale Dataset of Object deTection in Aerial
images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset
contains 1,793,658 object instances of 18 categories of oriented-bounding-box
annotations collected from 11,268 aerial images. Based on this large-scale and
well-annotated dataset, we build baselines covering 10 state-of-the-art
algorithms with over 70 configurations, where the speed and accuracy
performances of each model have been evaluated. Furthermore, we provide a
uniform code library for ODAI and build a website for testing and evaluating
different algorithms. Previous challenges run on DOTA have attracted more than
1300 teams worldwide. We believe that the expanded large-scale DOTA dataset,
the extensive baselines, the code library and the challenges can facilitate the
designs of robust algorithms and reproducible research on the problem of object
detection in aerial images.
