Dataset Viewer

The dataset viewer is not available for this dataset.

Cannot get the config names for the dataset.

Error code:   ConfigNamesError
Exception:    FileNotFoundError
Message:      Couldn't find any data file at /src/services/worker/RingoWRW97/UAV-GeoLoc. Couldn't find 'RingoWRW97/UAV-GeoLoc' on the Hugging Face Hub either: LocalEntryNotFoundError: An error happened while trying to locate the file on the Hub and we cannot find the requested files in the local cache. Please check your connection and try again or make sure your Internet connection is on.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1027, in dataset_module_factory
                  raise FileNotFoundError(
              FileNotFoundError: Couldn't find any data file at /src/services/worker/RingoWRW97/UAV-GeoLoc. Couldn't find 'RingoWRW97/UAV-GeoLoc' on the Hugging Face Hub either: LocalEntryNotFoundError: An error happened while trying to locate the file on the Hub and we cannot find the requested files in the local cache. Please check your connection and try again or make sure your Internet connection is on.

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YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

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:

git lfs install
git clone https://huggingface.co/datasets/RingoWRW97/UAV-GeoLoc

2.combine and extract the files

# 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.

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