SpikeViMFormer 2025 [paper][model] [Cite]

Efficient Spike-driven Transformer for High-performance Drone-View Geo-Localization

Zhongwei Chen^1,2,3, Haijun Rong^1,2,3, Zhaoxu Yang*^1,2,3, Guoqi Li*^4,5,6

¹School of Aerospace Engineering, Xi'an Jiaotong University China
²State Key Laboratory for Strength and Vibration of Mechanical Structures
³Shaanxi Key Laboratory of Environment and Control for Flight Vehicle
⁴Institute of Automation, Chinese Academy of Sciences, China
⁵School of Artificial Intelligence, University of Chinese Academy of Sciences
⁶Peng Cheng Laboratory

This repository is the official implementation of the paper "Efficient Spike-driven Transformer for High-performance Drone-View Geo-Localization". The current version of the repository can cover the experiments reported in the paper, for researchers in time efficiency. And we will also update this repository for better understanding and clarity.

📚 Table of contents

• TODOs
• Dataset Access
• Dataset Structure
• Train and Test
• Pre-trained Checkpoints
• License
• Acknowledgments
• Citation

📜 TODOs

• Release the training code
• Release the evaluation (testing) code
• Release the pretrained weights of SpikeViMFormer

💾 Dataset Access

Please prepare University-1652, SUES-200

📁 Dataset Structure

University-1652 Dataset Directory Structure

├── University-1652/
│   ├── train/
│       ├── drone/                   /* drone-view training images 
│           ├── 0001
|           ├── 0002
|           ...
│       ├── satellite/               /* satellite-view training images       
│   ├── test/
│       ├── query_drone/  
│       ├── gallery_drone/  
│       ├── query_satellite/  
│       ├── gallery_satellite/ 

SUES-200 Dataset Directory Structure

├─ SUES-200
  ├── Training
    ├── 150/
    ├── 200/
    ├── 250/
    └── 300/
  ├── Testing
    ├── 150/
    ├── 200/ 
    ├── 250/	
    └── 300/

🚀 Train and Test

For University-1652 Dataset

Train: run train_university.py, with --only_test = False.

Test: run train_university.py, with --only_test = True, and choose the model in --ckpt_path.

For SUES-200 Dataset

Train: run train_SUES-200.py, with --only_test = False.

Test: run train_SUES-200.py, with --only_test = True, and choose the model in --ckpt_path.

🤗 Pre-trained Checkpoints

We provide the trained models in the link below:

Baidu Netdisk Link: [] 提取码:

Google Drive Link: [https://drive.google.com/drive/folders/1l_cMkAlHdEytL7SCkZkynEiIRTKvcNBQ?usp=drive_link]

We will update this repository for better clarity ASAP, current version is for quick research for researchers interested in the cross-view geo-localization task.

🎫 License

This project is licensed under the Apache 2.0 license.

📌 Citation

If you find this code useful for your research, please cite our papers.

@article{chen2025efficient,
  title={Efficient Spike-driven Transformer for High-performance Drone-View Geo-Localization},
  author={Chen, Zhongwei, Yang, Zhao-Xu, Rong, Hai-Jun, Guoqi Li},
  journal={arXiv preprint arXiv:2512.19365},
  year={2025}
}

🙏 Acknowledgments

This repository is built using the DAC, Meta-SpikeFormer, E-SpikeFormer repositories. Thanks for their wonderful work.