  We consider the problem of cross-view geo-localization. The primary challenge
of this task is to learn the robust feature against large viewpoint changes.
Existing benchmarks can help, but are limited in the number of viewpoints.
Image pairs, containing two viewpoints, e.g., satellite and ground, are usually
provided, which may compromise the feature learning. Besides phone cameras and
satellites, in this paper, we argue that drones could serve as the third
platform to deal with the geo-localization problem. In contrast to the
traditional ground-view images, drone-view images meet fewer obstacles, e.g.,
trees, and could provide a comprehensive view when flying around the target
place. To verify the effectiveness of the drone platform, we introduce a new
multi-view multi-source benchmark for drone-based geo-localization, named
University-1652. University-1652 contains data from three platforms, i.e.,
synthetic drones, satellites and ground cameras of 1,652 university buildings
around the world. To our knowledge, University-1652 is the first drone-based
geo-localization dataset and enables two new tasks, i.e., drone-view target
localization and drone navigation. As the name implies, drone-view target
localization intends to predict the location of the target place via drone-view
images. On the other hand, given a satellite-view query image, drone navigation
is to drive the drone to the area of interest in the query. We use this dataset
to analyze a variety of off-the-shelf CNN features and propose a strong CNN
baseline on this challenging dataset. The experiments show that University-1652
helps the model to learn the viewpoint-invariant features and also has good
generalization ability in the real-world scenario.
