| ---
|
| 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:
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| - aerial-vision
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| - uav
|
| - depth
|
| - research-dataset
|
| task_categories:
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| - feature-extraction
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| language:
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| - en
|
| size_categories:
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| - 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
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| - Code: https://github.com/nudt-sawlab/AirZoo
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| - Benchmarks: https://huggingface.co/datasets/RingoWRW97/AirZoo-Real
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| - Website: https://nudt-sawlab.github.io/AirZoo/
|
|
|
| ## Citation
|
|
|
| ```bibtex
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| @article{cheng2026airzoo,
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| title={AirZoo: A Unified Large-Scale Dataset for Grounding Aerial Geometric 3D Vision},
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| 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},
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| journal={arXiv preprint arXiv:2604.26567},
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| year={2026}
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| }
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| ```
|
|
|
