News

• 2021-07-06 LUAI, a contest of object in aerial images on ICCV 2021, is now open. New
• 2021-02-25 The new benchmark DOTA-v2.0 , including dataset , code library and 70 baselines, is released. New
• 2019-12-31 iSAID Evaluation server is online.
• 2019-06-14 "A large-scale dataset for instance segmentation in aerial images" ( iSAID) has been released.
• 2019-06-14 The code of “Learning RoI Transformer for Detecting Oriented Objects in Aerial Images” has released.
• 2019-03-05 DOTA-v1.5 has released.
• 2018-04-30 The code of Faster-RCNN OBB is released.
• 2018-03-16 The registration for ODAI is open now.
• 2018-03-8 DOTA development kit ia available now. It's helpful to play on DOTA!
• 2018-02-19 The article of DOTA has been accepted by CVPR'2018.
• 2018-02-08 ODAI：a contest of object detection in aerial images on ICPR'2018, is now open!
• 2018-01-26 DOTA-v1.0 released with all images and oriented bounding box annotations for training and vallidation!

Description

DOTA is a large-scale dataset for object detection in aerial images. It can be used to develop and evaluate object detectors in aerial images. The images are collected from different sensors and platforms. Each image is of the size in the range from 800 × 800 to 20,000 × 20,000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. The instances in DOTA images are annotated by experts in aerial image interpretation by arbitrary (8 d.o.f.) quadrilateral. We will continue to update DOTA, to grow in size and scope to reflect evolving real-world conditions. Now it has three versions:

• DOTA-v1.0 contains 15 common categories, 2,806 images and 188, 282 instances. The proportions of the training set, validation set, and testing set in DOTA-v1.0 are 1/2, 1/6, and 1/3, respectively.
• DOTA-v1.5 uses the same images as DOTA-v1.0, but the extremely small instances (less than 10 pixels) are also annotated. Moreover, a new category, ”container crane” is added. It contains 403,318 instances in total. The number of images and dataset splits are the same as DOTA-v1.0. This version was released for the DOAI Challenge 2019 on Object Detection in Aerial Images in conjunction with IEEE CVPR 2019.
• DOTA-v2.0 collects more Google Earth, GF-2 Satellite, and aerial images. There are 18 common categories, 11,268 images and 1,793,658 instances in DOTA-v2.0. Compared to DOTA-v1.5, it further adds the new categories of ”airport” and ”helipad”. The 11,268 images of DOTA are split into training, validation, test-dev, and test-challenge sets. To avoid the problem of overfitting, the proportion of training and validation set is smaller than the test set. Furthermore, we have two test sets, namely test-dev and test-challenge. Training contains 1,830 images and 268,627 instances. Validation contains 593 images and 81,048 instances. We released the images and ground truths for training and validation sets. Test-dev contains 2,792 images and 353,346 instances. We released the images but not the ground truths. Test-challenge contains 6,053 images and 1,090,637 instances. The images and ground truths of test-challenge will be available only during the challenging.

Examples of Annotated Images

@ARTICLE{9560031,
  author={Ding, Jian and Xue, Nan and Xia, Gui-Song and Bai, Xiang and Yang, Wen and Yang, Michael and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title={Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges},
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2021.3117983}}

@InProceedings{Xia_2018_CVPR,
author = {Xia, Gui-Song and Bai, Xiang and Ding, Jian and Zhu, Zhen and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
title = {DOTA: A Large-Scale Dataset for Object Detection in Aerial Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}

@InProceedings{Ding_2019_CVPR,
author = {Jian Ding, Nan Xue, Yang Long, Gui-Song Xia, Qikai Lu},
title = {Learning RoI Transformer for Detecting Oriented Objects in Aerial Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
	

Communicate

For any problem you have in using DOTA, you can join the QQ group and communicate.

You could also ask questions and comment on the github issues.

Contact

If you have any the problem or feedback in using DOTA, please contact

• Jian Ding at jian.ding@whu.edu.cn,
• Gui-Song Xia at guisong.xia@whu.edu.cn,