{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dota-a-large-scale-dataset-for-object","title":"DOTA: A Large-scale Dataset for Object Detection in Aerial Images","arxiv_id":"1711.10398","date":"2017-11-28","proceeding":"CVPR 2018 6","authors":["Gui-Song Xia","Xiang Bai","Jian Ding","Zhen Zhu","Serge Belongie","Jiebo Luo","Mihai Datcu","Marcello Pelillo","Liangpei Zhang"],"abstract":"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.","url_abs":"https://arxiv.org/abs/1711.10398v3","url_pdf":"https://arxiv.org/pdf/1711.10398v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dota-a-large-scale-dataset-for-object","repo_url":"https://github.com/CAPTAIN-WHU/DOTA_devkit","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"dota-a-large-scale-dataset-for-object","repo_url":"https://github.com/jessemelpolio/Faster_RCNN_for_DOTA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"unanswered"}},{"paper_slug":"dota-a-large-scale-dataset-for-object","repo_url":"https://github.com/ming71/UCAS-AOD-benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"dota-a-large-scale-dataset-for-object","repo_url":"https://github.com/taegoobot/aerial-object-detector","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"dota-a-large-scale-dataset-for-object","repo_url":"https://github.com/PaddlePaddle/PaddleDetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":null},{"paper_slug":"dota-a-large-scale-dataset-for-object","repo_url":"https://github.com/open-mmlab/mmrotate","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"earth-observation","task_name":"Earth Observation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-in-aerial-images","task_name":"Object Detection In Aerial Images"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[{"slug":"dota","name":"DOTA","full_name":"Dataset for Object deTection in Aerial Images"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-in-aerial-images-on-dota-1","task":"Object Detection In Aerial Images","dataset":"DOTA","model":"FR-O (DOTA)","rank_in_archive_order":58,"of":58,"metrics":{"mAP":"52.93%"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.10398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.10398"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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