Datasets:

ckyrkou
/

aid

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label aid@3e884b62a66fbd1d6cbf55064c74d3b37f3eb39c
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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2386, 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 2303, 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 2178, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1460, 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 1483, 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 1158, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1095, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1116, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label aid@3e884b62a66fbd1d6cbf55064c74d3b37f3eb39c

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AID Dataset (Aerial Image Dataset)

Overview

The Aerial Image Dataset (AID) is a large-scale benchmark for aerial scene classification. The dataset contains 10,000 RGB aerial images collected from Google Earth imagery and annotated into 30 semantic scene categories. It was created to advance research in remote sensing and aerial image understanding.

Statistics

Property	Value
Images	10,000
Classes	30
Resolution	600 × 600 pixels
Source	Google Earth Imagery

Classes

Airport
Bare Land
Baseball Field
Beach
Bridge
Center
Church
Commercial
Dense Residential
Desert
Farmland
Forest
Industrial
Meadow
Medium Residential
Mountain
Park
Parking
Playground
Pond
Port
Railway Station
Resort
River
School
Sparse Residential
Square
Stadium
Storage Tanks
Viaduct

Dataset Characteristics

High intra-class variability
Images collected from different countries and regions
Multiple spatial resolutions
Variations in illumination, viewpoint, orientation, and scale
Expert-annotated labels

Applications

Aerial Scene Classification
Remote Sensing
Land-Use Classification
Earth Observation
Deep Learning Research
Computer Vision Benchmarking

Source

Official dataset page:

https://captain-whu.github.io/AID/

Citation

@article{xia2017aid,
  title={AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification},
  author={Xia, Gui-Song and Hu, Jingwen and Hu, Fan and Shi, Baoguang and Bai, Xiang and Zhong, Yanfei and Zhang, Liangpei and Lu, Xiaoqiang},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={55},
  number={7},
  pages={3965--3981},
  year={2017},
  publisher={IEEE}
}

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