README.md

metadata

license: other
pretty_name: AirZoo Data
gated: true
extra_gated_heading: Request access to AirZoo Data
extra_gated_description: >-
  For non-commercial research or education. Access is granted automatically
  after you agree.
extra_gated_button_content: Agree and request access
extra_gated_prompt: >
  Access is for non-commercial research or education only. Do not redistribute
  the dataset. Follow Cesium and related provider terms for map-derived content.
extra_gated_fields:
  Full name: text
  Affiliation / Institution: text
  Country: country
  Advisor / PI name: text
  Intended use:
    type: select
    options:
      - Research
      - Education
      - label: Other
        value: other
  Brief project description: text
  I agree to non-commercial use only: checkbox
  I will not redistribute the data: checkbox
tags:
  - aerial-vision
  - uav
  - depth
  - research-dataset
task_categories:
  - feature-extraction
language:
  - en
size_categories:
  - 1T<n<10T

AirZoo Data

Synthetic aerial training renders from the AirZoo project (RGB, metric depth, and poses).

This repo is gated. Request access on this page before downloading. Approval is automatic after you agree.

Renders were produced with Cesium for Unreal, Unreal Engine, and AirSim. Their licenses and terms still apply. Access here does not allow redistribution of the data.

Contents

• AirZoo_TrainingData/ — sequence archives

Related

• Engine: https://huggingface.co/datasets/choyaa/AirZoo-Engine
• Code: https://github.com/nudt-sawlab/AirZoo
• Benchmarks: https://huggingface.co/datasets/RingoWRW97/AirZoo-Real
• Website: https://nudt-sawlab.github.io/AirZoo/

Citation

@article{cheng2026airzoo,
  title={AirZoo: A Unified Large-Scale Dataset for Grounding Aerial Geometric 3D Vision},
  author={Cheng, Xiaoya and Wu, Rouwan and Liu, Xinyi and Cui, Zeyu and Liu, Yan and Zhao, Na and Liu, Yu and Zhang, Maojun and Yan, Shen},
  journal={arXiv preprint arXiv:2604.26567},
  year={2026}
}