University-1652 is a multi-view multi-source benchmark for drone-based geo-localization that contains 1652 buildings of 72 universities around the world. We provide images collected from the virtual drone, the satellite and the ground.
Task 1: Drone-view target localization. (Drone -> Satellite)} Given one drone-view image or video, the task aims to find the most similar satellite-view image to localize the target building in the satellite view.
Task 2: Drone navigation. (Satellite -> Drone)} Given one satellite-view image, the drone intends to find the most relevant place (drone-view images) that it has passed by. According to its flight history, the drone could be navigated back to the target place.
We provide our generated images and make a large-scale synthetic dataset called DG-Market. This dataset is generated by our DG-Net (https://arxiv.org/abs/1904.07223) and consists of 128,307 images (613MB), about 10 times larger than the training set of original Market-1501 (even much more can be generated with DG-Net). It can be used as a source of unlabeled training dataset for semi-supervised learning. You may download the dataset from Google Drive (or Baidu Disk password: qxyh).