The dataset viewer is not available for this split.
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.
GeoBench-2 Dataset License Attribution
Dataset Name: m-bigearthnetv2
Original Dataset Name: BigEarthNet v2
Original Source: https://bigearth.net
Related Publication(s): https://arxiv.org/abs/2407.03653
Licensing
- Annotation License: CDLA-Permissive-1.0 (Community Data License Agreement – Permissive, Version 1.0)
- Image License: Sentinel (Copernicus) open (public domain / open access)
- Declared By Original Provider: https://bigearth.net (and their PDF “Description_BigEarthNet_v2.pdf”)
Redistribution Status in GeoBench-2
This dataset is redistributed under the same license terms as declared by the original providers.
ServiceNow Research, IBM Research, TUM, and AI Alliance do not claim ownership or grant new rights beyond those already specified.
Attribution Requirements
When using this dataset (directly or through GeoBench-2), please cite the original dataset creators and reference both the dataset page and publication:
Clasen, Kai Norman, Lukas Hackel, Tobias Burgert, Gencer Sumbul, Begüm Demir, and Volker Markl.
“reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis.” arXiv preprint arXiv:2407.03653, 2024.
Additionally, you may cite the original BigEarthNet paper:
Sumbul, G., Charfuelan, M., Demir, B., & Markl, V.
“BigEarthNet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding.” CoRR, abs/1902.06148, 2019.
License Disclaimer & Takedown Policy
GEO-Bench-2 redistributes transformed versions of publicly available datasets for research and benchmarking purposes. Each dataset included here is redistributed under the same license terms designated by its original licensor.
No New Rights Created: ServiceNow Research, IBM Research, TUM, and AI Alliance do not claim ownership over these datasets and do not grant any new rights beyond those already specified by the original licensors.
Attribution: All credit remains with the original dataset creators and providers. Each dataset is accompanied by license and attribution information pointing to the original source.
Disclaimer of Responsibility: The consortium partners act only as redistributors. We do not independently verify the accuracy of the license terms supplied by original dataset providers and disclaim liability for any errors, omissions, or misattributions in those designations.
Takedown Procedure: If you are a rights holder and believe that a dataset has been misattributed, incorrectly licensed, or should not be redistributed in this form, please contact Paolo Fraccaro ([email protected]) and Alexandre Lacoste ([email protected]). We will promptly review and, if necessary, remove or modify access to the dataset.
By accessing or using GEO-Bench-2, you agree to comply with the original dataset licenses and acknowledge that the responsibility for verifying appropriate use lies with the end-user.
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