University-1652: Drone-based Geo-localization Benchmark 🚁

University-1652 is a multi-view dataset for drone-based geo-localization, annotating 1652 buildings across 72 universities (ACM Multimedia 2020, paper). Cited in 50+ papers, it supports Drone → Satellite localization and Satellite → Drone navigation.

Dataset Structure

• Splits:
  • Train: 50,218 images (drone, satellite, street, google; 33 universities)
  • Test:
    • query_drone: 37,855 images
    • gallery_drone: 51,355 images
    • query_street: 2,579 images
    • gallery_street: 2,921 images
    • query_satellite: 701 images
    • gallery_satellite: 951 images
    • 4K_drone: 12 images
• Features:
  • image: Drone/satellite/street/4K_drone images
  • building_id: Building identifier
  • view_type: drone/satellite/street/drone_4k
  • split_type: train/query/gallery
• Size: ~9.2GB (unzipped)

Usage

from datasets import load_dataset
ds = load_dataset("layumi/university-1652", split="train")
ds[0]  # {'image': ..., 'building_id': '0001', 'view_type': 'drone', 'split_type': 'train'}