Large special-events parking involves various parking scenarios, e.g., temporary parking and on-street parking. Their occupancy detection is challenging as it is unrealistic to construct gates/stations for temporary parking areas or build a sensor-based detection system to cover every single street. To address this issue, this study develops a quadrotor-enabled autonomous parking occupancy detection system. A camera-equipped quadrotor is flying over the parking lot first; then the images are captured by the on-board camera of the quadrotor and transferred to the ground station; finally, the ground station will process and release the occupancy information to the driver's mobile devices. The decision tree learning algorithm is adopted to determine the optimal flying speed for the quadrotor to balance the trade-off between the detection efficiency and accuracy. In order to tackle the complex environment in real-life parking, a convolutional neural network (CNN)-based vehicle detection model has been trained and implemented, where the realistic factors, e.g., passing pedestrians and tree blocking, are considered. Experiments are conducted to illustrate the effectiveness of the proposed system.