## 3 Dataset  
To solve the lack of datasets mentioned in the Introduction, we collect and annotate our own dataset and name it VRAI_AIR, which means vehicle re-identification in aerial imagery made by Aerospace Information Research Institute, Chinese Academy of Science. To develop the diversity of the UAV-based vehicle ReID datasets, different from the DJI Phantom 4 for the VRAI dataset, we choose DJI Mavic Air 2 as the image acquisition platform, which has longer flight time, larger field of view and HDR (High Dynamic Range Imaging) mode. We fly two DJI Mavic Air 2 drones along Huairou campus of UCAS for a long distance. The location is carefully chosen because the road is located in the suburban area with moderate traffic flow and vehicle speed which avoid blur and occlusion, containing a diversity of vehicle types such as trucks and lorries. We make the drones fly as long a distance as possible to simulate situations that might be encountered in a real deployment. There are trees, street signs and overpass on the road, and parking lot, pedestrian cross-walk, crossroads and no-parking area during the long-distance flight, which provides a convenience to capture images with complex background, as shown in Figure 5.  
During the flight, the drones work in the video mode of FHD HDR with 1920 \(\times\) 1080 resolution and 30 fps, and the flight-altitude ranges from 30 m to 90 m. We control the drones to fly in various modes, including hovering, circling, following, rapid ascent, rapid descent, etc., while controlling the cameras to swing in order to get diverse images of vehicles. After the flight, we obtain the videos captured by the drones. Considering the average speed of the vehicles and the frame rate of the videos, we took one frame from every four frames as the material for annotation, and get a total of 1304 panoramic images. We use LabelMe [32] to label the vehicles in panoramic images with axis-aligned bounding boxes. By aligning the real time of the two UAV videos and labeling them frame by frame, we are able to match vehicles with the same ID in different panoramic images with consistent labels. Furthermore, we count the images attributes to make better use of the dataset. We obtain 1783 vehicle slices of 202 IDs in total, details shown in Figure 6. Each ID owns 8.8 images on average, the most owns 56 images, the least owns 2 images, and most of the IDs own between 3 and 12 images, the detailed Image/ID situation is shown in Figure 7a. Although our method does not use additional information for inference, we still labeled the dataset with additional information to facilitate the research of others, including colors and vehicle types. Regarding the color information, it is worth noting that (1) \"gray\" cars is categorized as \"silver\" cars because it is difficult to tell the difference between them; (2) for cases where the vehicle has more than one color (e.g., large spray paint and stickers), we only label the color that occupies the largest area of the body; (3) all colors are listed and no color is classified as \"other\". The detailed statistics of the color information is shown in Figure 7b, from which it's easy to see that the exterior color of the vehicle is mostly black, white and silver. As for as the type information, it is worth noting that \"sedan\" refers to three compartments cars, \"hatchback\" refers to two compartments cars including SUV, \"lorry\" here refers to small trucks and \"truck\" here refers to large trucks, \"other\" often means some tool vehicles, such as concrete mixer trucks, etc. The detailed statistics of the car type information is shown in Figure 7c, it can be seen that, although we chose the non-urban road to shoot, but sedan and hatchback still account for the majority. We also count the image resolution of the dataset and compare it with the VRAI dataset, and the resolution distribution of the two is shown in Figure 7d.