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Datasets:
choyaa
/
AirZoo

Tasks:
Feature Extraction
Languages:
English
ArXiv:
Tags:
uav-geolocalization
navigation
drone-imagery
airzoo
weather-augmentation
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Dataset card Files
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Dataset Viewer
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label Argentina
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label Argentina

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Dataset Card for AirZoo Dataset

Total Size: Approximately 4–5 TB (estimated; upload in progress). Status: ⏳ Uploading...

Sequence Card

  • Multi-Weather Trajectories: Each sequence contains 3–4 weather trajectories (e.g., sunny, cloudy, rainy, night) following the same flight path at the same altitude and pitch configuration.
  • Data Modalities: Sequential frames are provided as *_0.png for RGB and *_1.npz for metric depth (float32, lossless conversion from PF depth).
  • Pose & Metadata: Per-trajectory pose files (*.txt) and optional overlap validation files (*_validated.txt) are included in each sequence archive.
  • Archive Layout: One .zip per sequence under <Country>/<Sequence>.zip (e.g., Argentina/Argentina_seq1.zip).
  • Data Access:
    • Images & Depth: Download sequence .zip archives from this Hugging Face dataset under AirZoo_TrainingData/<Country>/<Sequence>.zip.
    • Index: See airzoo_sequence_index.json in this repository for the full variant list and frame counts.
  • 🙋❗Note: Please download the reprojection code from this repo and run the demo first to verify the projection alignment and coordinate system.
Sequence Name Location (Region) Lat / Lng Height Pitch Range Weather Variants # Trajs
Argentina_seq1 Argentina -34.6114 / -58.4206 300m [0,30], [0] cloudy, night, rainy, sunset 4
Argentina_seq3 Argentina -34.8274 / -58.5578 200m [0,30], [0] cloudy, night, sunny, sunset 4
Argentina_seq5 Argentina -34.9033 / -57.9652 200m [0,30], [0] cloudy, foggy, night, sunny 4
Argentina_seq6 Argentina -32.9390 / -60.6799 300m [0], [30,60] foggy, night, sunny, sunset 4
Athens_seq1 Athens, Greece 37.9759 / 23.7111 600m [0,30], [0] cloudy, foggy, night, rainy 4
Athens_seq2 Athens, Greece 38.0251 / 23.7748 500m [0], [30,60] foggy, night, rainy 4
Athens_seq3 Athens, Greece 40.6607 / 22.9541 700m [0,30], [0] foggy, rainy, sunny, sunset 4
Athens_seq4 Athens, Greece 35.5201 / 24.0168 400m [0], [30,60] night, rainy, sunny, sunset 4
Athens_seq5 Athens, Greece 35.3447 / 25.1367 100m [0,30], [0] foggy, night, rainy, sunset 4
Australia_seq11 Australia -32.8336 / 151.3103 800m [0], [60,80] foggy, night, rainy, sunset 4
Australia_seq2 Australia -37.7973 / 144.9450 500m [0], [30,60] night, rainy, sunny, sunset 4
Australia_seq3 Australia -32.4337 / 115.7706 200m [0,30], [0] cloudy, night, sunny, sunset 4
Australia_seq4 Australia -27.9779 / 153.4251 500m [0], [30,60] cloudy, foggy, night, sunset 4
Australia_seq6 Australia -33.9226 / 151.1706 300m [0], [30,60] night, rainy, sunny, sunset 4
Australia_seq7 Australia -33.9165 / 151.2098 500m [0,30], [0] foggy, night, sunny, sunset 4
Australia_seq9 Australia -35.2780 / 149.1294 500m [0,30], [0] cloudy, night, sunny, sunset 4
Austria_seq1 Austria 48.2217 / 16.3674 800m [0], [60,80] cloudy, foggy, night, sunny 4
Brazil_seq1 Brazil -15.7880 / -47.9012 300m [0,30], [0] night, rainy, sunny, sunset 4
Brazil_seq10 Brazil -3.1137 / -59.9768 600m [0], [30,60] cloudy, night, rainy, sunset 4
Brazil_seq11 Brazil -1.4025 / -48.4707 800m [0], [60,80] foggy, night, rainy, sunset 4
Brazil_seq3 Brazil -22.9145 / -43.2395 100m [0], [30,60] cloudy, foggy, night, rainy 4
Brazil_seq7 Brazil -22.9832 / -43.3935 700m [0,30], [0] foggy, night, rainy, sunny 4
Brazil_seq8 Brazil -23.5683 / -46.7424 400m [0], [30,60] cloudy, foggy, night, sunny 4
Bulgaria_seq1 Bulgaria 42.7147 / 23.3266 300m [0], [60,80] cloudy, foggy, night, sunset 4
Canada_seq1 Canada 43.6343 / -79.4266 300m [0,30], [0] night, rainy, sunny, sunset 4
Canada_seq2 Canada 45.4835 / -73.5382 500m [0], [30,60] cloudy, night, sunny, sunset 4
Canada_seq3 Canada 49.1976 / -123.2083 200m [0,30], [0] night, rainy, sunny, sunset 4
Canada_seq4 Canada 53.5285 / -113.5261 600m [0], [30,60] cloudy, foggy, sunny, sunset 4
Canada_seq5_1 Canada 45.4364 / -75.7253 700m [0] cloudy, night 2
Canada_seq6 Canada 49.8837 / -97.1609 800m [0], [30,60] night, rainy, sunny, sunset 4
Czech_seq1 Czech Republic 50.0812 / 14.4045 700m [0], [60,80] cloudy, rainy, sunny, sunset 4
Denmark_seq1 Denmark 55.7157 / 12.5855 400m [0,30], [0] foggy, night, rainy, sunny 4
Denmark_seq3 Denmark 55.6350 / 12.6542 800m [0,30], [0] foggy, night, sunny, sunset 4
England_seq1 England, UK 51.5038 / -0.1944 300m [0,30], [0] night, rainy, sunny, sunset 4
England_seq11 England, UK 54.7103 / -1.2114 300m [0], [60,80] foggy, rainy, sunny, sunset 4
England_seq2 England, UK 51.4713 / -0.4983 500m [0], [30,60] night, rainy, sunny, sunset 4
England_seq3 England, UK 51.8705 / -2.2599 200m [0,30], [0] cloudy, night, sunny, sunset 4
England_seq4 England, UK 55.9467 / -3.2414 500m [0], [30,60] cloudy, night, sunny, sunset 4
England_seq5 England, UK 53.4302 / -2.9966 200m [0,30], [0] cloudy, night, sunny, sunset 4
HongKong_seq1 Hong Kong, China 22.2981 / 114.1554 300m [0,30], [0] foggy, night, sunny, sunset 4
HongKong_seq2 Hong Kong, China 22.2812 / 114.1785 500m [0], [30,60] cloudy, night, sunny 3
HongKong_seq3 Hong Kong, China 22.2948 / 114.1775 200m [0,30], [0] foggy, night, sunny, sunset 4
HongKong_seq4 Hong Kong, China 22.4104 / 114.2163 500m [0], [30,60] foggy, night, sunny, sunset 4
Hungary_seq1 Hungary 47.5114 / 19.0321 400m [0], [60,80] foggy, night, rainy, sunny 4
Iceland_seq1 Iceland 64.1590 / -22.0172 100m [0,30], [0] cloudy, foggy, night, sunset 4
Iceland_seq2 Iceland 64.1050 / -21.7769 700m [0], [30,60] cloudy, foggy, night, sunny 4
Iceland_seq3 Iceland 63.8923 / -22.4926 700m [0], [60,80] night, rainy, sunny, sunset 4
Iceland_seq4 Iceland 64.2274 / -21.0598 400m [0], [60,80] cloudy, foggy, night, sunset 4
Japan_seq10 Japan 33.5554 / 130.3501 100m [0], [30,60] cloudy, foggy, rainy, sunset 4
Japan_seq2 Japan 35.3785 / 138.8549 500m [0], [30,60] night, rainy, sunny, sunset 4
Japan_seq3 Japan 35.6582 / 139.7416 200m [0,30], [0] foggy, rainy, sunny, sunset 4
Japan_seq5 Japan 35.5614 / 139.7586 200m [0,30], [0] foggy, night, rainy, sunset 4
Japan_seq6 Japan 35.4367 / 139.6286 300m [0], [30,60] foggy, night, sunny, sunset 4
Japan_seq8 Japan 34.3714 / 132.4448 300m [0], [30,60] foggy, night, sunny, sunset 4
Japan_seq9 Japan 34.8261 / 134.6686 500m [0,30], [0] night, rainy, sunny, sunset 4
Macao_seq1 Macao, China 22.1724 / 113.5594 200m [0,30], [0] cloudy, night, sunny, sunset 4
Mexico_seq1 Mexico 19.4438 / -99.1390 600m [0,30], [0] cloudy, rainy, sunny, sunset 4
Mexico_seq2 Mexico 19.4457 / -99.0568 700m [0], [30,60] foggy, rainy, sunny, sunset 4
Mexico_seq3 Mexico 19.3267 / -99.1999 800m [0,30], [0] cloudy, foggy, night, sunset 4
Mexico_seq4 Mexico 20.6936 / -103.3479 400m [0], [30,60] cloudy, night, rainy, sunset 4
Mexico_seq5 Mexico 25.6679 / -100.3052 100m [0,30], [0] cloudy, night, rainy, sunset 4
Newzealand_seq1 New Zealand -36.9734 / 174.7721 300m [0,30], [0] night, rainy, sunny, sunset 4
Newzealand_seq4 New Zealand -43.5466 / 172.5134 500m [0], [30,60] cloudy, night, sunny, sunset 4
Newzealand_seq6 New Zealand -37.0179 / 174.7691 300m [0], [30,60] cloudy, night, rainy, sunset 4
Newzealand_seq7 New Zealand -45.1187 / 170.9593 500m [0,30], [0] cloudy, foggy, sunny, sunset 4
Newzealand_seq8 New Zealand -41.2991 / 174.7618 300m [0], [30,60] foggy, night, sunny, sunset 4
Newzealand_seq9 New Zealand -45.8655 / 170.4534 500m [0,30], [0] cloudy, night, sunny, sunset 4
Norway_seq11 Norway 58.0687 / 7.7876 500m [0], [60,80] cloudy, night, rainy, sunny 4
Poland_seq1 Poland 52.2355 / 21.0389 200m [0], [60,80] foggy, night, rainy, sunny 4
Singapore_seq1 Singapore 1.2978 / 103.7764 400m [0,30], [0] cloudy, foggy, night, sunny 4
Singapore_seq11 Singapore 1.3731 / 103.8337 700m [0], [60,80] cloudy, foggy, night, sunset 4
Singapore_seq2 Singapore 1.2954 / 103.8473 600m [0], [30,60] cloudy, foggy, rainy, sunset 4
SouthAfrica_seq10 South Africa -33.9005 / 18.3990 800m [0], [30,60] cloudy, night, rainy, sunny 4
SouthAfrica_seq11 South Africa -33.7929 / 18.4613 600m [0], [60,80] foggy, night, sunny, sunset 4
SouthAfrica_seq2 South Africa -26.1728 / 28.0414 500m [0], [30,60] foggy, night, sunny, sunset 4
SouthAfrica_seq4 South Africa -25.7332 / 28.1695 500m [0], [30,60] cloudy, night, sunny, sunset 4
SouthAfrica_seq6 South Africa -26.1210 / 28.0369 400m [0], [30,60] foggy, rainy, sunny, sunset 4
SouthAfrica_seq7 South Africa -33.9592 / 18.5805 100m [0,30], [0] cloudy, foggy, sunny, sunset 4
SouthAfrica_seq8 South Africa -26.2469 / 27.8933 700m [0], [30,60] cloudy, foggy, sunny, sunset 4
Switzerland_seq1 Switzerland 46.6764 / 7.8291 600m [0,30], [0] cloudy, night, rainy, sunny 4
Switzerland_seq11 Switzerland 47.3128 / 7.9340 100m [0], [60,80] cloudy, foggy, rainy, sunny 4
Switzerland_seq2 Switzerland 46.7376 / 8.1654 800m [0], [30,60] cloudy, rainy, sunny, sunset 4
Switzerland_seq3 Switzerland 46.1024 / 7.9481 400m [0,30], [0] foggy, night, rainy, sunset 4
Taiwan_seq1 Taiwan, China 25.0212 / 121.5356 300m [0,30], [0] foggy, night, sunny, sunset 4
Taiwan_seq11 Taiwan, China 24.9405 / 121.2087 200m [0], [60,80] cloudy, night, rainy, sunny 4
Taiwan_seq2 Taiwan, China 25.0319 / 121.4580 500m [0], [30,60] cloudy, night, sunny, sunset 4
Taiwan_seq4 Taiwan, China 24.0873 / 120.6788 500m [0], [30,60] foggy, night, sunny, sunset 4
USA_seq11 USA 39.1816 / -96.5582 400m [0], [60,80] cloudy, foggy, rainy, sunset 4
USA_seq12 USA 25.7661 / -80.1350 600m [0], [60,80] cloudy, night, rainy, sunny 4
USA_seq3 USA 42.3514 / -71.1369 200m [0,30], [0] cloudy, night, sunny, sunset 4
USA_seq4 USA 47.6270 / -122.3633 600m [0], [30,60] foggy, night, sunny, sunset 4
USA_seq5 USA 39.9299 / -75.1545 700m [0,30], [0] cloudy, foggy, sunny, sunset 4
USA_seq6 USA 41.2054 / -112.0662 800m [0], [30,60] cloudy, foggy, sunny, sunset 4
USA_seq7 USA 27.0912 / -82.4349 100m [0,30], [0] cloudy, foggy, night, sunny 4
USA_seq8 USA 34.0375 / -118.2847 400m [0], [30,60] foggy, night, sunny, sunset 4

Citation

If you find this dataset useful, please cite:

@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}
}

Licensing Information

Copyright (c) 2026 Saw Lab, National University of Defense Technology (NUDT).

The Dataset is provided for non-commercial research and educational purposes only.

Terms of Use:

  • Attribution: You must provide appropriate credit to Saw Lab, NUDT and cite the corresponding paper in any derivative works or publications.
  • Non-Commercial: Commercial use, including but not limited to selling the data or using it to train commercial models, is strictly prohibited.
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