# crossview

**Repository Path**: xdjiangkai/crossview

## Basic Information

- **Project Name**: crossview
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-09-08
- **Last Updated**: 2025-09-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MCCG

This repository contains the dataset link and the code for our paper [MCCG: A ConvNeXt-based Multiple-Classifier Method for Cross-view Geo-localization](https://ieeexplore.ieee.org/document/10185134), IEEE Transactions on Circuits and Systems for Video Technology. Thank you for your kindly attention.


## Requirement
1. Download the [University-1652](https://github.com/layumi/University1652-Baseline) dataset
2. Download the [SUES-200](https://github.com/Reza-Zhu/SUES-200-Benchmark) dataset
3. Configuring the environment
   * First you need to configure the torch and torchision from the [pytorch](https://pytorch.org/) website
   * ```shell
     pip install -r requirement.txt
     ```

## About dataset
The organization of the dataset.

More detailed about Univetsity-1652 dataset structure:
```
├── University-1652/
│   ├── train/
│       ├── drone/                   /* drone-view training images 
│           ├── 0001
│           ├── 0002
│           ...
│       ├── street/                  /* street-view training images 
│       ├── satellite/               /* satellite-view training images       
│       ├── google/                  /* noisy street-view training images (collected from Google Image)
│   ├── test/
│       ├── query_drone/  
│       ├── gallery_drone/  
│       ├── query_street/  
│       ├── gallery_street/ 
│       ├── query_satellite/  
│       ├── gallery_satellite/ 
│       ├── 4K_drone/
```
More detailed about SUES-200 dataset structure:
```
├── SUES-200/
│   ├── train/
│       ├── 150/
│           ├── drone/                   /* drone-view training images 
│               ├── 0001
│               ├── 0002
│               ...
│           ├── satellite/               /* satellite-view training images       
│       ├── 200/                  
│       ├── 250/  
│       ├── 300/  
│   ├── test/
│       ├── 150/  
│           ├── query_drone/  
│           ├── gallery_drone/  
│           ├── query_satellite/  
│           ├── gallery_satellite/ 
│       ├── 200/  
│       ├── 250/  
│       ├── 300/  
```


## Train and Test
We provide scripts to complete MCCG training and testing
* Change the **data_dir** and **test_dir** paths in **run.sh** and then run:
```shell
bash run.sh
```

## Citation

```bibtex
@ARTICLE{Shen2024MCCG,
  author={Shen, Tianrui and Wei, Yingmei and Kang, Lai and Wan, Shanshan and Yang, Yee-Hong},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={MCCG: A ConvNeXt-Based Multiple-Classifier Method for Cross-View Geo-Localization}, 
  year={2024},
  volume={34},
  number={3},
  pages={1456-1468},
  keywords={Feature extraction;Drones;Task analysis;Image segmentation;Semantics;Satellites;Data mining;Cross-view;ConvNeXt;image retrieval;multiple feature representation},
  doi={10.1109/TCSVT.2023.3296074}}
```