Datasets:
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Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Invalid value. in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
df = pandas_read_json(f)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
return json_reader.read()
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
obj = self._get_object_parser(self.data)
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
self._parse()
~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
ujson_loads(json, precise_float=self.precise_float), dtype=None
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4523, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2768, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2972, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2483, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 327, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
HyperKD-RS: hypergraphical knowledge-distillation targets for remote sensing
Teacher harvest for distilling a tiny promptable segmenter (PicoSAM3) along exact hypergraph edges, plus the multispectral series the neural codec ("Planet-JEPA") trains on. Built for the OpenCV AI Competition 2026 entry Places Not Pixels (Team Picasso).
Unlike HyperKD (MobileSAM2), which infers hyperedges from embedding distances, the hyperedges here are exact:
- temporal — Hungarian identity tracks across up to 41 annual passes (1985–2025); each track collapsed into a per-pixel majority-vote consensus mask that serves as the training target,
- granularity — all three SAM scale masks (s0 subpart / s1 part / s2 whole) per prompt, QC-gated per scale, plus laminar containment parents between chosen-scale masks.
Source
Landsat 4/5/7/8/9 (Microsoft Planetary Computer STAC), 4.8 km windows at 30 m, best annual pass per spot, SLC-off striping screened out (≤5% nodata). Teacher: SAM 2.1 hiera-base-plus (int8), grid-prompted at 8×8 + half-stride densify — the production tokenizer of the entry.
Layout
One shard per AOI: shards/shard_<tag>.npz
| array | shape | meaning |
|---|---|---|
images |
(S,256,256,3) u8 | display RGB per pass |
multispec |
(S,160,160,4) f16 | raw reflectance r,g,b,nir08 |
dates, platforms |
(S,) str | exact acquisition date, platform |
masks |
(M,256,256) u8 | all QC-passing scale masks + consensus masks |
samples |
(N,11) f32 | one row per accepted prompt |
samples columns: scene, cx, cy, s0, s1, s2, chosen, track, parent, target, is_consensus — s0..s2 index into masks (−1 = failed that scale's QC),
chosen is the production pick, parent the laminar containment parent
within the pass (−1 = top-level), target the distillation target
(track consensus where the track has ≥2 observations), cx,cy in the
800×800 prompt frame.
Labels are never baked into geometry: concept annotations (SAM 3.1,
calibrated per concept via the taxonomy's visual prompts and thresholds)
ship separately as INFERRED facts.
Follow along
The complete pipeline — fetchers, tri-scale harvester, SAM 3.1 labeller,
PicoSAM3 distillation harness, taxonomy, AOI manifest, and the QC audit
trail — ships in harness/ (also bundled as
harness.zip); run order is in its README.
AOIs
32 spots across 12 domains (farmland, grazing, aquaculture, coastal, fire,
flood, protected, water, urban/infrastructure, mining, forestry, nuisance) —
complete: 32 annual shards (1,217 scenes, 1985–2025) + 6 event shards
(_ev, 73 monthly passes inside documented fire/flood windows), every shard
with a matching labels/labels_<tag>.npz (SAM 3.1 INFERRED concepts +
per-scene cloud masks).
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