# 3D Positioning of Drones through Images
## 2 Related Work
### UL14 plus Dataset  
Currently, commonly used datasets for cross-view matching geolocation include the CVUSA dataset, CVACT dataset, and University1652 dataset. Although there are many mature cross-view geolocation datasets available for geolocation tasks, there are few relevant datasets that can be applied to visual localization on UAV platforms. While the recent University1652 pioneered the inclusion of UAV perspective images in cross-view geolocation datasets, its inherent limitations prevent its application to pure visual geolocation tasks for UAVs. Firstly, the images in the University1652 dataset are collected from buildings at universities around the world, making them spatially discrete (Figure 1). In contrast, the ground images captured by onboard cameras for UAV autonomous positioning in GNSS environments, as proposed in this paper, are spatially continuous. Secondly, the majority of the content in this dataset's images consists of buildings, while actual UAV visual localization scenes include many elements such as roads and vegetation in addition to buildings. Finally, to save labor costs in the creation of the University1652 dataset, the UAV perspective images were collected from 3D model buildings in Google Earth. Although the modeling detail in Google Earth is high, there are still significant differences compared to images captured by optical cameras in real-world scenarios. Therefore, although the University1652 dataset introduced UAV platform perspective images and proposed a UAV perspective target localization task, it cannot serve as a dataset for the UAV cross-view matching geolocation task described in this paper. To address the deficiencies in existing geolocation datasets and to cater to the characteristics of the UAV cross-view matching geolocation algorithm proposed in this paper, we introduce the UL14 plus (University Localization14 plus) dataset. Based on the DenseUAV and UL14 datasets, it aims to support UAV self-localization and altitude determination. Previous datasets like DenseUAV and UL14 focused more on latitude and longitude information for positioning, with less emphasis on altitude data. The UL14 dataset is a reconstruction based on the DenseUAV dataset. Unlike DenseUAV's image retrieval task, UL14 is tailored for the FPI benchmark task. It employs a data augmentation method that allows search images to have both random scale and random target distribution characteristics. Two variables were introduced to crop satellite images: a center point coverage C (initial value 0.75) and a scale range (512-1000). C determines the spacing of UAV images within the satellite image. A smaller C concentrates UAV images in the central region, while a larger C disperses them. The scale parameter determines the satellite image's scale in actual space. A larger scale means each pixel represents a greater actual spatial distance. This data augmentation method aims to enhance the model's resistance to scale changes and offsets. UL14 plus is developed based on UL14, specifically for the multi-altitude localization/altitude determination benchmark proposed in this paper. UAV images were captured using a DJI Phantom 4 Pro drone, flying at altitudes of 120-200 m above ground (in 20-m intervals), as shown on the right side of Figure 2. The DJI Pilot program's waypoint flight feature was used for photography, ensuring shots were taken at the same waypoint positions across different altitudes. Choosing university areas for shooting offers two advantages: 1. The buildings and other landscape features in universities are distinct, facilitating subsequent feature extraction by deep learning models; 2. Buildings on campuses are generally not too tall, ensuring the safety of drone flight paths during image capture. To ensure spatial continuity and dense sampling in the dataset construction, the drone's flight path design considered sampling density, with a sampling distance of 20 m, contributing to the uniform distribution of the dataset.