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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@d27de6ff5270422d52e8673f6fc2d92509530bfd
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2092, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2197, in cast_table_to_features
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1995, in cast_array_to_feature
                  return feature.cast_storage(array)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1169, in cast_storage
                  [self._strval2int(label) if label is not None else None for label in storage.to_pylist()]
                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1098, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label oscd100@d27de6ff5270422d52e8673f6fc2d92509530bfd

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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.

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