README.md

metadata

license: mit
task_categories:
  - robotics
  - reinforcement-learning
language:
  - en
pretty_name: VoLN-UAV Dataset

VoLN-UAV Dataset

This Hugging Face dataset entry is intended for the navigation data used by VoLN-UAV. The environment assets are hosted separately so that users can fetch the simulator package and the trajectory data independently.

Hugging Face Entries

• env: https://huggingface.co/datasets/Louj/VoLN-UAV-ENV
• dataset: https://huggingface.co/datasets/Louj/VoLN-UAV-Dataset

Data Organization

The release package provides the benchmark inputs required by VoLN-UAV:

• scene-level Train/Validation/Test split manifests;
• route JSON files with RGB frame references and pose-derived state fields;
• benchmark metadata for scenes, episodes, and checksums;
• optional copied RGB frames under source/frames/ when the package is built in copy mode.

Usage

1. Download the dataset package and the env package.
2. Unzip the dataset package.
3. Set source_root in the benchmark config to the unzipped source/ directory.
4. Run python -m voln_uav.cli.build_benchmark --config <config.yaml>.

The generated manifest.json contains the release summary and Hugging Face resource links.

Recommended Citation

Please cite the VoLN-UAV paper and this dataset repository once the manuscript metadata is finalized.

Download Layout

The dataset is uploaded as independent ZIP shards under metadata/, train/, val/, and test/. Each shard is below 5 GB. Extract metadata/VoLN-UAV-metadata.zip first, then extract the split shards you need into the same directory so that paths such as source/frames/<scene>/<trajectory>/<frame>.png match the JSONL metadata.

The train, validation, and test splits are episode-disjoint. The test split is held out on a separate scene, while train and validation may share scenes.

Use SHA256SUMS.txt to verify downloaded shards.