# UAV-GeoLoc
This repo contains the large-vocabulary datasets for our paper *UAV-GeoLoc: A Large-vocabulary Dataset and Geometry-Transformed
Method for UAV Geo-Localization*. Given a top-down UAV image, retrieve a corresponding satellite image patch to infer the UAV's location.

## 📦 Usage && Dataset Structure 

### ⬇️ Download Instructions

You need to download all parts of a category (e.g., all `Country.zip.00X` files) **before extraction**.

#### 1. Clone the repository with Git LFS enabled:

```bash
git lfs install
git clone https://huggingface.co/datasets/RingoWRW97/UAV-GeoLoc
```
#### 2.combine and extract the files
```bash
# For Country
cat Country.zip.* > Country.zip
unzip Country.zip

# For Terrain
cat Terrain.zip.* > Terrain.zip
unzip Terrain.zip

# For Rot
unzip Rot.zip
```

### 🖼️ Dataset Structure
Each folder under Country or Terrain (e.g., USA, Italy, Japan, etc.) contains *N* scenes for that region. Each scene is structured as follows:  
Country/  
├── Australia/  
├────City(Sydney)  
├──────Region   
├──────── DB / (Satellite Map)  
├──────── query / (24 dir,including height 100:150:25, heading 0:360:45)  
├──────── semi_positive.json  
├──────── positive.json   
├── Brazil/  
├── USA/  
└── ...  
### 🗂️ Index.zip (Train/Val/Test Splits)
The dataset includes a compressed file `Index.zip` that contains various .txt files used to define training, validation, and test splits across different components of the dataset.  
After extracting `Index.zip`, the structure looks like:
Index/  
├── train.txt  
├── train_all.txt  
├── train_country.txt  
├── train_db.txt  
├── train_db_all.txt  
├── train_db_country.txt  
├── train_query.txt  
├── train_query_all.txt  
├── train_query_country.txt  
├── train_query_test.txt  
├── val.txt  
├── val_all.txt  
├── val_country.txt  
├── val_db.txt  
├── val_db_country.txt  
├── val_query.txt  
├── val_query_country.txt  
├── test.txt  
├── test_all.txt  
├── test_country.txt  
├── test_db.txt  
├── test_query.txt  

Each file defines a specific subset of the dataset used for:
- `*_query.txt`: UAV query images  
- `*_db.txt`: Reference DB images  
- `*_country.txt`: only train on `country` class
- `*.txt`: only train on `terrain` class
- `*_all.txt`: Union of all images in a given category  

## 📸 Result on Rot
![Result](rot_1.png)
‘Fire’ denotes results trained on our proposed dataset. ‘Box’ indicates that the model is trained with the LPN method.

## 📄 License
This dataset is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).  
Any modification of the dataset is strictly prohibited. The imagery was collected using Google Earth Studio, and appropriate attribution to Google must be provided in any derivative work or publication.