# 3D Positioning of Drones through Images
## 2 Related Work
### UL14 plus Dataset
Additionally, overlapping portions in images from adjacent shooting points help the model enhance its ability to discern subtle differences. As shown on the left side of Figure 2, three groups of images represent scenes captured at the T \(-\) 1, T, and T + 1 waypoints. Each image overlaps with adjacent images. In previous image retrieval methods, these three images might have been localized to the same position. However, in the point-by-image localization algorithm proposed in this paper, the centers of these three images should be localized to three different geographical locations. Therefore, using a dense sampling method to create the dataset helps the subsequently designed cross-view matching geolocation algorithm learn the offset differences between images, enabling the model to develop a strong spatial position information perception ability. This contributes to improving the model's localization accuracy and forms the basis for the altitude determination method proposed in this paper. During data collection, the UAV's gimbal was pointed vertically downward, capturing vertical overhead views at each waypoint. For different altitudes at the same waypoint, horizontal offset was controlled within 1 m, and vertical offset within 0.5 m. The gimbal camera had an FOV of 84\({}^{\circ}\) and a focal length of 8.8 mm, with the zoom set to minimum during shooting. Photo dimensions were 4864 \(\times\) 3648 pixels. Training and test data were collected from four universities in Zhejiang, China. The training set at 120-200 m altitude includes 712 sampling points, 3560 UAV images, and 3560 satellite images. At 80-100 m altitude, a portion of the UL14 dataset was selected, including 334 sampling points, 1002 UAV images, and 1002 satellite images. In the training  
Figure 1: University 1652 dataset. set, the ratio of satellite to UAV images is 1:1. The UL14 plus dataset still includes some content from the UL14 dataset, which is significant because: First, it includes UAV perspective images from multiple altitudes, thus training a model more adaptable to altitude changes; Second, the new and old UAV images differ noticeably in capture time, style, and weather conditions, helping to enhance the model's generalization ability. UAV images were captured in real scenes at various flight altitudes. The resolution of UAV images is (4864, 3648). To improve the model's adaptability to multiple altitudes, we crop UAV images from different heights with different parameters to simulate images from more altitudes. We aim to build a dataset encompassing ground features under more conditions. Figure 3 below shows the cropping situation for UAV images at 200 m altitude, where c is the cropping ratio. All cropped images share a common center point. The crop size gradually decreases. In subsequent model calculations, these UAV images will be reshaped to a uniform size of 256 \(\times\) 256. Therefore, the larger cropped images will contain a wider range of ground features, while smaller cropped images will contain more ground details. This improves the model's adaptability to multi-scale features. When UAV images are from 80 to 100 m, no cropping is conducted to ensure sufficient large-scale ground features in the images. Figure 3: Cropped display of images collected at a height of 200 m.The red dot represents the center of the drone’s shooting, and the box represents the maximum square area that can be captured from the drone image. Figure 2: Sampling waypoints and altitude display. Satellite views use Google Maps' level 20 images. Based on the actual latitude and longitude of the UAV shots, the UAV shooting point is located on a satellite image covering the entire school. Centered on this point, a (4000, 4000) satellite image is cropped and reshaped to a resolution of (512, 512). The test set at 120-200 m altitude includes 120 sampling points, 600 UAV images, and 7200 satellite images. The test set at 80-100 m altitude includes part of the UL14 dataset's test set, with 100 sampling points, 300 UAV images, and 3600 satellite images.