oscd100

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Split (1)

train

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 oscd100@60a75e11e45632199b206e472f6647ed2b4beaba
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 oscd100@60a75e11e45632199b206e472f6647ed2b4beaba

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OSCD100

OSCD100 is a subset of the Onera Satellite Change Detection (OSCD) dataset, containing 100 pre-cropped image pairs at 256x256 resolution. It is designed for tutorials and demonstrations, not benchmarking.

Dataset Structure

The dataset maintains the same file structure and all 13 Sentinel-2 bands as the full OSCD dataset:

Bands: B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B10, B11, B12
Splits: train (60 samples), val (20 samples), test (20 samples)
Format: GeoTIFF for imagery, PNG for labels
Resolution: 256x256 pixels per crop
Temporal: Each sample contains two images (pre and post change)

Files

Onera Satellite Change Detection dataset - Images.zip (220MB): All image pairs with 13 Sentinel-2 bands
Onera Satellite Change Detection dataset - Train Labels.zip: Training labels
Onera Satellite Change Detection dataset - Val Labels.zip: Validation labels
Onera Satellite Change Detection dataset - Test Labels.zip: Test labels

Usage

from torchgeo.datasets import OSCD100

# Load dataset
dataset = OSCD100(root='data', split='train', download=True)

# Get a sample
sample = dataset[0]
print(sample['image'].shape)  # torch.Size([2, 13, 256, 256])
print(sample['mask'].shape)   # torch.Size([1, 256, 256])

Citation

If you use this dataset, please cite the original OSCD paper:

@inproceedings{daudt2018urban,
  title={Urban change detection for multispectral earth observation using convolutional neural networks},
  author={Daudt, Rodrigo Caye and Le Saux, Bertr and Boulch, Alexandre},
  booktitle={IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium},
  pages={2115--2118},
  year={2018},
  organization={IEEE}
}

License

Same as the original OSCD dataset.

Downloads last month: 6

Total file size:

230 MB