| # 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 | |
|  | |
| ‘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. | |