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

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

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

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.

Dataset Attributes

Label SVG
TasksDetection
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CategoriesGeospatial, Earth Observation
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SensorAerial Imagery

Class Labels

Baseball DiamondBasketball CourtBridgeHarborHelicopterLarge VehiclePlaneRoundaboutShipSmall VehicleSoccer FieldStorage TankSwimming PoolTennis CourtTrack Field