  Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable
positioning. However, due to limited satellite coverage or communication
disruptions, UAVs may lose signals from satellite-based positioning systems. In
such situations, vision-based techniques can serve as an alternative, ensuring
the self-positioning capability of UAVs. However, most of the existing datasets
are developed for the geo-localization tasks of the objects identified by UAVs,
rather than the self-positioning task of UAVs. Furthermore, the current UAV
datasets use discrete sampling on synthetic data, such as Google Maps, thereby
neglecting the crucial aspects of dense sampling and the uncertainties commonly
experienced in real-world scenarios. To address these issues, this paper
presents a new dataset, DenseUAV, which is the first publicly available dataset
designed for the UAV self-positioning task. DenseUAV adopts dense sampling on
UAV images obtained in low-altitude urban settings. In total, over 27K UAV-view
and satellite-view images of 14 university campuses are collected and
annotated, establishing a new benchmark. In terms of model development, we
first verify the superiority of Transformers over CNNs in this task. Then, we
incorporate metric learning into representation learning to enhance the
discriminative capacity of the model and to lessen the modality discrepancy.
Besides, to facilitate joint learning from both perspectives, we propose a
mutually supervised learning approach. Last, we enhance the Recall@K metric and
introduce a new measurement, SDM@K, to evaluate the performance of a trained
model from both the retrieval and localization perspectives simultaneously. As
a result, the proposed baseline method achieves a remarkable Recall@1 score of
83.05% and an SDM@1 score of 86.24% on DenseUAV. The dataset and code will be
made publicly available on https://github.com/Dmmm1997/DenseUAV.
