shanmuga12nivetha/university-1652

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

<image-card alt="Sample" src="https://raw.githubusercontent.com/layumi/University1652-Baseline/master/docs/index_files/Data.jpg" ></image-card>

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'}