Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
input_stats: struct<image_s2: struct<band_names: list<item: string>, normalization_mode: string, mean: list<item: double>, std: list<item: double>, var: list<item: double>, min: list<item: double>, max: list<item: double>, count: double, histograms: list<item: list<item: double>>, histogram_bins: list<item: double>, pct_02: list<item: double>, pct_98: list<item: double>, shift_offsets: list<item: double>, norm_mean: list<item: double>, norm_std: list<item: double>, norm_var: list<item: double>, clip_min_used: int64, clip_max_used: int64>, image_s1: struct<band_names: list<item: string>, normalization_mode: string, mean: list<item: double>, std: list<item: double>, var: list<item: double>, min: list<item: double>, max: list<item: double>, count: double, histograms: list<item: list<item: double>>, histogram_bins: list<item: double>, pct_02: list<item: double>, pct_98: list<item: double>, shift_offsets: list<item: double>, norm_mean: list<item: double>, norm_std: list<item: double>, norm_var: list<item: double>, clip_min_used: int64, clip_max_used: int64>>
target_stats: struct<class_counts: list<item: double>, total_samples: int64, num_classes: int64, class_frequencies: list<item: double>, multi_label: bool, class_names: list<item: string>>
vs
input_stats: struct<image_s2: struct<band_names: list<item: string>, normalization_mode: string, mean: list<item: double>, std: list<item: double>, var: list<item: double>, min: list<item: double>, max: list<item: double>, count: double, histograms: list<item: list<item: double>>, histogram_bins: list<item: double>, pct_02: list<item: double>, pct_98: list<item: double>, shift_offsets: list<item: double>, norm_mean: list<item: double>, norm_std: list<item: double>, norm_var: list<item: double>>, image_s1: struct<band_names: list<item: string>, normalization_mode: string, mean: list<item: double>, std: list<item: double>, var: list<item: double>, min: list<item: double>, max: list<item: double>, count: double, histograms: list<item: list<item: double>>, histogram_bins: list<item: double>, pct_02: list<item: double>, pct_98: list<item: double>, shift_offsets: list<item: double>, norm_mean: list<item: double>, norm_std: list<item: double>, norm_var: list<item: double>>>
target_stats: struct<class_counts: list<item: double>, total_samples: int64, num_classes: int64, class_frequencies: list<item: double>, multi_label: bool, class_names: list<item: string>, labels_per_sample: double, samples_per_class_count: list<item: double>, label_co_occurrence: list<item: list<item: double>>, samples_with_no_labels: double, label_conditional_probabilities: list<item: list<item: double>>>
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
return next(iter(self.iter(batch_size=n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
for key, example in iterator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
for key, pa_table in self._iter_arrow():
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 559, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
input_stats: struct<image_s2: struct<band_names: list<item: string>, normalization_mode: string, mean: list<item: double>, std: list<item: double>, var: list<item: double>, min: list<item: double>, max: list<item: double>, count: double, histograms: list<item: list<item: double>>, histogram_bins: list<item: double>, pct_02: list<item: double>, pct_98: list<item: double>, shift_offsets: list<item: double>, norm_mean: list<item: double>, norm_std: list<item: double>, norm_var: list<item: double>, clip_min_used: int64, clip_max_used: int64>, image_s1: struct<band_names: list<item: string>, normalization_mode: string, mean: list<item: double>, std: list<item: double>, var: list<item: double>, min: list<item: double>, max: list<item: double>, count: double, histograms: list<item: list<item: double>>, histogram_bins: list<item: double>, pct_02: list<item: double>, pct_98: list<item: double>, shift_offsets: list<item: double>, norm_mean: list<item: double>, norm_std: list<item: double>, norm_var: list<item: double>, clip_min_used: int64, clip_max_used: int64>>
target_stats: struct<class_counts: list<item: double>, total_samples: int64, num_classes: int64, class_frequencies: list<item: double>, multi_label: bool, class_names: list<item: string>>
vs
input_stats: struct<image_s2: struct<band_names: list<item: string>, normalization_mode: string, mean: list<item: double>, std: list<item: double>, var: list<item: double>, min: list<item: double>, max: list<item: double>, count: double, histograms: list<item: list<item: double>>, histogram_bins: list<item: double>, pct_02: list<item: double>, pct_98: list<item: double>, shift_offsets: list<item: double>, norm_mean: list<item: double>, norm_std: list<item: double>, norm_var: list<item: double>>, image_s1: struct<band_names: list<item: string>, normalization_mode: string, mean: list<item: double>, std: list<item: double>, var: list<item: double>, min: list<item: double>, max: list<item: double>, count: double, histograms: list<item: list<item: double>>, histogram_bins: list<item: double>, pct_02: list<item: double>, pct_98: list<item: double>, shift_offsets: list<item: double>, norm_mean: list<item: double>, norm_std: list<item: double>, norm_var: list<item: double>>>
target_stats: struct<class_counts: list<item: double>, total_samples: int64, num_classes: int64, class_frequencies: list<item: double>, multi_label: bool, class_names: list<item: string>, labels_per_sample: double, samples_per_class_count: list<item: double>, label_co_occurrence: list<item: list<item: double>>, samples_with_no_labels: double, label_conditional_probabilities: list<item: list<item: double>>>Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
License:
GeoBenchV2 Version of the BigEarthNetV2 dataset, that is released under the CDLA-Permissive-1.0 License.
Citation:
If you use this dataset, please cite the original dataset paper by Clasen et al. 2025.
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