UAV-OBB: An aerial urban vehicle dataset with oriented bounding boxes for remote sensing object detection in smart cities

Urban smart city traffic management increasingly relies on UAV-based sensing, yet many widely used drone datasets annotate vehicles with axis-aligned bounding boxes that include unnecessary background and do not encode vehicle orientation. We present UAV-OBB, an aerial urban vehicle dataset with oriented bounding boxes (OBBs), designed for rotation-aware computer vision object detection and traffic monitoring from predominantly nadir-view UAV imagery. UAV-OBB contains 1617 RGB images at 1920 × 1080 resolution captured over roads in Chongqing and Wuhan (China), together with OBB Nannotations in YOLOv8-OBB label format and supplementary MP4 evaluation videos. The dataset provides 46,807 oriented annotations across six vehicle classes: bike, bus, car, other_vehicle, taxi, and truck, and is split into 1383 training images, 218 validation images, and 16 test images. Data were collected at 75 to 108 m altitude under diverse real-world conditions, including morning, midday, evening, night, rain, and mist or light fog, with both wide field-of-view and zoom settings to introduce strong scale variation. All instances were manually annotated using rotation-capable tools and double-checked for consistency, and occluded and truncated vehicles were included when the majority of the object was visible. To support practical smart city evaluation beyond static mAP, UAV-OBB also includes a short video clip with sparsely annotated reference frames and a longer unannotated sequence for qualitative assessment of temporal stability and deployment behaviour. UAV-OBB provides a realistic benchmark for rotation-aware detection, tracking, counting, and traffic flow analysis in urban UAV surveillance scenarios.