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 (Download Only)

Since University-1652 is a standard multi-view vision benchmark with a strict directory structure, this repository is intended for raw file downloading only. You do not need to load it via datasets.load_dataset(). Instead, download the raw folders and plug them directly into your standard PyTorch DataLoader (e.g., torchvision.datasets.ImageFolder).

Method 1: Using Hugging Face Python API (Recommended)

This is the most reliable way to download the entire dataset with proper resume-on-failure support.

# pip install huggingface_hub
from huggingface_hub import snapshot_download

# Download the dataset and keep the original folder structure (train/ and test/)
local_dir = snapshot_download(
    repo_id="layumi/university-1652",
    repo_type="dataset",
    local_dir="./University-1652",  # Your local destination directory
    local_dir_use_symlinks=False    # Download actual files instead of symlinks
)
print(f"Dataset successfully downloaded to: {local_dir}")

Method 2: Git Clone

Ensure you have Git LFS installed on your system before cloning.

git lfs install
git clone https://huggingface.co/datasets/layumi/university-1652

⚠️ Terms of Use & Legal Disclaimer (Research Only & Google Maps Data)

1. Research Purpose Only

This dataset is provided strictly for non-commercial academic research purposes. Any commercial utilization, including but not limited to training commercial models, commercial image retrieval systems, or integration into proprietary software, is strictly prohibited.

2. Google Maps Data Fair Usage & Claim

• Data Origin & Ownership: This dataset contains imagery crawled from and property of Google Maps/Earth. All copyright, intellectual property rights, and trademarks associated with these images remain the sole property of Google LLC and its content providers.
• Fair Use Notice: The incorporation of these images into University-1652 is intended solely for academic benchmarking and scientific advancement under "Fair Use" guidelines.
• No Endorsement: This dataset is an independent academic project and is not endorsed, sponsored, or affiliated with Google LLC.
• Redistribution: Users are prohibited from isolating, extracting, or utilizing the Google Maps imagery for any purposes outside the benchmark tasks defined in the official publication.

🏆 Citation

If you find this dataset or the benchmark helpful for your research, please consider citing the original paper:

@inproceedings{zheng2020university,
  title={University-1652: A Multi-view Dataset for Drone-based Geo-localization},
  author={Zheng, Zhedong and Wei, Yunchao and Yang, Yi},
  booktitle={Proceedings of the paper ACM International Conference on Multimedia (ACM MM)},
  year={2020}
}