Papers › DOTA: A Large-scale Dataset for Object Detection in Aerial Images

DOTA: A Large-scale Dataset for Object Detection in Aerial Images

28 Nov 2017CVPR 2018 6arXiv:1711.10398archive 2025-07-28

Gui-Song Xia, Xiang Bai, Jian Ding, Zhen Zhu, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, Liangpei Zhang

Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the object instances on the earth's surface, but also due to the scarcity of well-annotated datasets of objects in aerial scenes. To advance object detection research in Earth Vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA). To this end, we collect $2806$ aerial images from different sensors and platforms. Each image is of the size about 4000-by-4000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. These DOTA images are then annotated by experts in aerial image interpretation using $15$ common object categories. The fully annotated DOTA images contains $188,282$ instances, each of which is labeled by an arbitrary (8 d.o.f.) quadrilateral To build a baseline for object detection in Earth Vision, we evaluate state-of-the-art object detection algorithms on DOTA. Experiments demonstrate that DOTA well represents real Earth Vision applications and are quite challenging.

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CAPTAIN-WHU/DOTA_devkit mentioned on GitHub report
jessemelpolio/Faster_RCNN_for_DOTA mentioned on GitHubmxnet report
ming71/UCAS-AOD-benchmark mentioned on GitHubpytorch report
open-mmlab/mmrotate pytorchApache-2.0 report

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Tasks

Earth ObservationObjectObject DetectionObject Detection In Aerial Imagesobject-detection

Datasets

Introduced by this paper, per the archive.

DOTA

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Object Detection In Aerial Images DOTA FR-O (DOTA) mAP 52.93% #58 of 58 Archive leaderboard report

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