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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1520, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 130, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 34, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1405, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 982, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 786, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1382, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1560, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
bands.b2 unknown | meta.json dict | __key__ string | __url__ string |
|---|---|---|---|
"BQGVBAAAMAAAAAQAcpEGAAAAAAAAAgUAAAAAAAAAAABQAAAAjBMAAMQlAQBjMgIArjoDACuaAwCc+AMAkVcEAKdeBQDsvAUAl9A(...TRUNCATED) | {
"fn": "patch_0.tif",
"lat": 41.331314594464914,
"lon": -124.65289421027032,
"patch_idx": 0,
"shard": 0
} | s2100k_00000_00000 | "hf://datasets/moham741/s2-100k-preprocessed@9f0fc67ccc373f1511ffcc07cff00fb1d731b968/train/s2100k_p(...TRUNCATED) |
"BQGVBAAAMAAAAAQAzkAHAAAAAAAAAgUAAAAAAAAAAABQAAAAGRcAAOEnAQBLQAIAcF0DAB3WAwCFTgQAv8QEABPJBQA5PQYAF1Y(...TRUNCATED) | {
"fn": "patch_1.tif",
"lat": 24.517396127704437,
"lon": 51.25335507855508,
"patch_idx": 1,
"shard": 0
} | s2100k_00000_00001 | "hf://datasets/moham741/s2-100k-preprocessed@9f0fc67ccc373f1511ffcc07cff00fb1d731b968/train/s2100k_p(...TRUNCATED) |
"BQGVBAAAMAAAAAQAMVoGAAAAAAAAAgUAAAAAAAAAAABQAAAADBUAAH7/AABv+AEAk/ACANdZAwB3wAMAqCYEAFwPBQAndgUALow(...TRUNCATED) | {
"fn": "patch_2.tif",
"lat": 42.96978266481537,
"lon": 112.24814910943044,
"patch_idx": 2,
"shard": 0
} | s2100k_00000_00002 | "hf://datasets/moham741/s2-100k-preprocessed@9f0fc67ccc373f1511ffcc07cff00fb1d731b968/train/s2100k_p(...TRUNCATED) |
"BQGVBAAAMAAAAAQAjQwIAAAAAAAAAgUAAAAAAAAAAABQAAAADhUAAP9fAQBFsAIAiQkEAGl5BACC5AQA6lUFACuYBgD5DAcA6iY(...TRUNCATED) | {
"fn": "patch_3.tif",
"lat": -17.116714107678078,
"lon": 19.01376703568776,
"patch_idx": 3,
"shard": 0
} | s2100k_00000_00003 | "hf://datasets/moham741/s2-100k-preprocessed@9f0fc67ccc373f1511ffcc07cff00fb1d731b968/train/s2100k_p(...TRUNCATED) |
"BQGVBAAAMAAAAAQAD/EGAAAAAAAAAgUAAAAAAAAAAABQAAAAbg8AAJsEAQBFBgIAoxQDAJN7AwAX7QMAbl0EAGGJBQCi/gUA1xY(...TRUNCATED) | {
"fn": "patch_4.tif",
"lat": 62.48120790473417,
"lon": -120.24154487318454,
"patch_idx": 4,
"shard": 0
} | s2100k_00000_00004 | "hf://datasets/moham741/s2-100k-preprocessed@9f0fc67ccc373f1511ffcc07cff00fb1d731b968/train/s2100k_p(...TRUNCATED) |
"BQGVBAAAMAAAAAQAZlYIAAAAAAAAAgUAAAAAAAAAAABQAAAAVxYAAJJgAQC3nAIAY/MDABNuBADf6gQAfGsFAMuzBgCONgcA2lE(...TRUNCATED) | {
"fn": "patch_5.tif",
"lat": 41.52931233897642,
"lon": 23.84533040068569,
"patch_idx": 5,
"shard": 0
} | s2100k_00000_00005 | "hf://datasets/moham741/s2-100k-preprocessed@9f0fc67ccc373f1511ffcc07cff00fb1d731b968/train/s2100k_p(...TRUNCATED) |
"BQGVBAAAMAAAAAQAJ8EGAAAAAAAAAgUAAAAAAAAAAABQAAAAahQAAG0JAQASCgIAEA4DAE+CAwCx9gMA8mkEAGR5BQDI8QUAKQg(...TRUNCATED) | {
"fn": "patch_6.tif",
"lat": -78.7128504914107,
"lon": -68.10819914082609,
"patch_idx": 6,
"shard": 0
} | s2100k_00000_00006 | "hf://datasets/moham741/s2-100k-preprocessed@9f0fc67ccc373f1511ffcc07cff00fb1d731b968/train/s2100k_p(...TRUNCATED) |
"BQGVBAAAMAAAAAQANjYGAAAAAAAAAgUAAAAAAAAAAABQAAAAtRIAAPT9AAD0/QEAR+kCADJLAwAJqgMAFQgEAMv7BACfWwUAzHE(...TRUNCATED) | {
"fn": "patch_7.tif",
"lat": 27.61675123466944,
"lon": 26.800695871255883,
"patch_idx": 7,
"shard": 0
} | s2100k_00000_00007 | "hf://datasets/moham741/s2-100k-preprocessed@9f0fc67ccc373f1511ffcc07cff00fb1d731b968/train/s2100k_p(...TRUNCATED) |
"BQGVBAAAMAAAAAQAa+cGAAAAAAAAAgUAAAAAAAAAAABQAAAA/BEAAPYaAQB3LAIA3UADAICkAwArDgQAM30EAHedBQBlCwYA3iM(...TRUNCATED) | {
"fn": "patch_8.tif",
"lat": 41.07076139176647,
"lon": -88.05157375699095,
"patch_idx": 8,
"shard": 0
} | s2100k_00000_00008 | "hf://datasets/moham741/s2-100k-preprocessed@9f0fc67ccc373f1511ffcc07cff00fb1d731b968/train/s2100k_p(...TRUNCATED) |
"BQGVBAAAMAAAAAQAS54GAAAAAAAAAgUAAAAAAAAAAABQAAAApBIAAEQHAQBoEAIAOwwDANB3AwCy3wMAq0YEAPlRBQAEuQUAf88(...TRUNCATED) | {
"fn": "patch_9.tif",
"lat": 20.07676591099624,
"lon": 24.649929305446044,
"patch_idx": 9,
"shard": 0
} | s2100k_00000_00009 | "hf://datasets/moham741/s2-100k-preprocessed@9f0fc67ccc373f1511ffcc07cff00fb1d731b968/train/s2100k_p(...TRUNCATED) |
End of preview.
S2-100K Preprocessed
Derived from torchgeo/s2-100k.
What changed
The original dataset stores patches as uint16 GeoTIFF files inside plain tar archives with no compression. This version converts every patch to a blosc2/zstd-compressed float32 array for faster I/O in training pipelines.
No season selection is performed. Each patch in the source dataset is a single Sentinel-2 L2A acquisition (no temporal dimension), so every patch is included as-is.
Source statistics
| Property | Value |
|---|---|
| Total patches | 100,000 |
| Shards | 100 (1,000 patches each) |
| Spatial size | 256 × 256 px (resampled to 10 m/px) |
| Spectral bands | 12 (B01–B09, B11, B12; no B10) |
| DN scale | L2A reflectance × 10000 (uint16 in source) |
Band order (index 0–11): B01, B02, B03, B04, B05, B06, B07, B08, B08A, B09, B11, B12
Format
WebDataset .tar shards under train/.
Each sample contains two files:
| File | Description |
|---|---|
{patch_id}.bands.b2 |
blosc2/zstd-compressed [12, 256, 256] float32 array |
{patch_id}.meta.json |
{"lon":…,"lat":…,"fn":…,"shard":…,"patch_idx":…} |
patch_id format: s2100k_{shard:05d}_{patch_idx:05d}
patch_idx is the shard-local 0-based index (0–999), matching the
patch_idx column in the source metadata.parquet.
e.g. s2100k_00003_00042 = shard 3, the 43rd patch in that shard (0-indexed).
Loading a sample
import blosc2, numpy as np, json, tarfile
N_CHANNELS, H, W = 12, 256, 256
with tarfile.open('s2100k_preprocessed_shard_00000.tar') as tf:
members = {m.name: m for m in tf.getmembers()}
patch_id = 's2100k_00000_00000'
raw = blosc2.decompress(tf.extractfile(members[f'{patch_id}.bands.b2']).read())
arr = np.frombuffer(raw, dtype=np.float32).reshape(N_CHANNELS, H, W)
meta = json.loads(tf.extractfile(members[f'{patch_id}.meta.json']).read())
print(arr.shape, arr.dtype, meta)
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