[Pattern Recognition] Multiple-environment Self-adaptive Network for Aerial-view Geo-localization

MuseNet

[Paper]

Prerequisites

• Python 3.6
• GPU Memory >= 8G
• Numpy > 1.12.1
• Pytorch 0.3+
• scipy == 1.2.1
• imgaug == 0.4.0

Getting started

Dataset & Preparation

Download University-1652 upon request. You may use the request template.

Download CVUSA.

Train & Evaluation

Train & Evaluation University-1652

sh run.sh

✨Download The Trained Model

Train & Evaluation CVUSA

python prepare_cvusa.py  
sh run_cvusa.sh

Citation

@ARTICLE{wang2024Muse,
  title={Multiple-environment Self-adaptive Network for Aerial-view Geo-localization}, 
  author={Wang, Tingyu and Zheng, Zhedong and Sun, Yaoqi and Yan, Chenggang and Yang, Yi and Tat-Seng Chua},
  journal = {Pattern Recognition},
  volume = {152},
  pages = {110363},
  year = {2024},
  doi = {https://doi.org/10.1016/j.patcog.2024.110363}}

@ARTICLE{wang2021LPN,
  title={Each Part Matters: Local Patterns Facilitate Cross-View Geo-Localization}, 
  author={Wang, Tingyu and Zheng, Zhedong and Yan, Chenggang and Zhang, Jiyong and Sun, Yaoqi and Zheng, Bolun and Yang, Yi},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  year={2022},
  volume={32},
  number={2},
  pages={867-879},
  doi={10.1109/TCSVT.2021.3061265}}

@article{zheng2020university,
  title={University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization},
  author={Zheng, Zhedong and Wei, Yunchao and Yang, Yi},
  journal={ACM Multimedia},
  year={2020}
}

Related Work