Accurate and reliable positioning and navigation are fundamental for the development of a wide number of applications. Despite in most of the regular working conditions the use of a GNSS (Global Navigation Satellite System) receiver is sufficient for properly determining the absolute coordinates of such a device, determining a reliable solution of the positioning problem in challenging conditions, e.g. in environments where GNSS is not available or not reliable, can be difficult. In such conditions, exploiting the information shared by different sensors and platforms can be useful for reliably determining the platforms’ positions. In this work, both ground and aerial platforms are considered: each platform is assumed to be provided with communication capabilities, which can be exploited to share its knowledge. In particular, since GNSS positioning is usually less effective at ground level than on a flying platform, here the aerial platforms are assumed to be provided with good GNSS-based positioning information. Instead, GNSS is assumed to be unavailable to the ground vehicles, which, instead, can use LiDAR/visual odometry for determining dead-reckoning solutions, Ultra Wide-Band (UWB) inter-platform ranging for relative positioning, and camera-based positions, provided by aerial platforms, for assessing their georeferenced positions. In particular, this work focuses on assessing the positioning performance when exploiting vision-based information about the georeferenced ground vehicle positions from a camera mounted on a Unmanned Aerial Vehicle (UAV). The camera acquired oblique views of the scene, while moving over the case study area during the test. YOLO network was used to detect cars from the image frames, whereas different techniques were used for obtaining the cars’ georeferenced coordinates, including the extraction of vehicle coordinates from 3D reconstructions obtained from the MoGe-2 network. Average errors at meter level on the determined georeferenced coordinates, even on vehicles not visible in the image acquired by the UAV, were obtained when combining UWB vehicle-to-vehicle ranges with MoGe-2 reconstructions.