AirGoal-10k

AirGoal-10k is an aerial image-goal navigation dataset released with UA-NWM: Uncertainty-Aware World Model for Aerial Image-Goal Navigation.

Project page: https://duryi.github.io/UA-NWM-Project-Page/
Code: https://github.com/DurYi/UA-NWM
Paper: https://arxiv.org/abs/2608.05597

Dataset Summary

AirGoal-10k contains 11,000 aerial navigation trajectories for image-goal navigation. Each trajectory contains 12 RGB observations and trajectory metadata. The test split additionally provides 32 candidate trajectories per case for offline ranking evaluation.

The dataset is stored under the airgoal10k/ subdirectory in this Hugging Face repository:

airgoal10k/
├── action_stats.json
├── pseudo_goals/
│   └── test/
├── train/
│   └── traj_000000..008999/
├── val/
│   └── traj_000000..000999/
└── test/
    └── traj_000000..000999/

Splits

Split	Number of trajectories	Notes
train	9,000	Training trajectories
val	1,000	Validation trajectories
test	1,000	Evaluation trajectories with 32 candidates per case

The repository also includes a small set of pseudo-goal images under airgoal10k/pseudo_goals/ for visualization and debugging.

File Format

Each trajectory folder contains 12 RGB frames and one metadata file:

traj_xxxxxx/
├── 0.jpg
├── 1.jpg
├── ...
├── 11.jpg
└── traj_data.pkl

traj_data.pkl is a Python pickle dictionary. For train/validation trajectories, it contains:

• point: (12, 3) array of 3D positions.
• roll, pitch, yaw: (12,) arrays of attitude angles.
• images: (12,) array of image identifiers.

For test trajectories, traj_data.pkl additionally contains:

• candidate_trajectories: a list of 32 candidate trajectory dictionaries, each with point, roll, pitch, and yaw fields.

action_stats.json stores the dataset-level action normalizer used by the UA-NWM codebase.

Download

From the root of the UA-NWM code repository, run:

hf download DurYi/AirGoal-10k \
  --repo-type dataset \
  --local-dir data \
  --include "airgoal10k/**"

The dataset is also available from Baidu Netdisk.

The expected local layout after downloading is:

data/
└── airgoal10k/
    ├── action_stats.json
    ├── pseudo_goals/
    ├── train/
    ├── val/
    └── test/

See the UA-NWM repository for environment setup, checkpoint preparation, training, evaluation, and visualization instructions.

Source and Acknowledgment

AirGoal-10k is built on top of AerialVLN and OpenFly. Please also respect the licenses and terms of the original data sources.

Citation

@misc{zhu2026uanwm,
      title={Uncertainty-Aware World Model for Aerial Image-Goal Navigation},
      author={Deyi Zhu and Haoyu Fan and Yinan Zhu and Weichen Zhang and Shilin Ma and Xinlei Chen and Yansong Tang},
      year={2026},
      eprint={2608.05597},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2608.05597},
}