Dataset Viewer
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: CastError
Message: Couldn't cast
version: string
name: string
description: string
points: int64
projection: string
hierarchy: struct<firstChunkSize: int64, stepSize: int64, depth: int64>
child 0, firstChunkSize: int64
child 1, stepSize: int64
child 2, depth: int64
offset: list<item: int64>
child 0, item: int64
scale: list<item: double>
child 0, item: double
spacing: double
boundingBox: struct<min: list<item: int64>, max: list<item: double>>
child 0, min: list<item: int64>
child 0, item: int64
child 1, max: list<item: double>
child 0, item: double
encoding: string
attributes: list<item: struct<name: string, description: string, size: int64, numElements: int64, elementSize: i (... 152 chars omitted)
child 0, item: struct<name: string, description: string, size: int64, numElements: int64, elementSize: int64, type: (... 140 chars omitted)
child 0, name: string
child 1, description: string
child 2, size: int64
child 3, numElements: int64
child 4, elementSize: int64
child 5, type: string
child 6, min: list<item: int64>
child 0, item: int64
child 7, max: list<item: double>
child 0, item: double
child 8, scale: list<item: int64>
child 0, item: int64
child 9, offset: list<item: int64>
child 0, item: int64
child 10, histogram: list<item: int64>
child 0, item: int64
variants: list<item: string>
child 0, item: string
classes_present: struct<returns_ood: struct<0: int64, 1: int
...
, 11: int64
child 12, 12: int64
child 13, 13: int64
child 14, 14: int64
child 3, m1_seedA: struct<0: int64, 1: int64, 2: int64, 3: int64, 4: int64, 5: int64, 6: int64, 7: int64, 8: int64, 9: (... 61 chars omitted)
child 0, 0: int64
child 1, 1: int64
child 2, 2: int64
child 3, 3: int64
child 4, 4: int64
child 5, 5: int64
child 6, 6: int64
child 7, 7: int64
child 8, 8: int64
child 9, 9: int64
child 10, 10: int64
child 11, 11: int64
child 12, 12: int64
child 13, 13: int64
child 14, 14: int64
files: struct<hierarchy.bin: struct<bytes: int64, sha256: string>, metadata.json: struct<bytes: int64, sha2 (... 62 chars omitted)
child 0, hierarchy.bin: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 1, metadata.json: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 2, octree.bin: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
dales_labels_included: bool
city: string
point_source: string
slots: list<item: struct<variant: string, fields: list<item: string>, packing: string>>
child 0, item: struct<variant: string, fields: list<item: string>, packing: string>
child 0, variant: string
child 1, fields: list<item: string>
child 0, item: string
child 2, packing: string
colour_source: null
area: string
to
{'area': Value('string'), 'points': Value('int64'), 'variants': List(Value('string')), 'encoding': Value('string'), 'city': Value('string'), 'point_source': Value('string'), 'colour_source': Value('null'), 'dales_labels_included': Value('bool'), 'slots': List({'variant': Value('string'), 'fields': List(Value('string')), 'packing': Value('string')}), 'attributes': List(Value('string')), 'classes_present': {'returns_ood': {'0': Value('int64'), '1': Value('int64'), '2': Value('int64'), '3': Value('int64'), '4': Value('int64'), '5': Value('int64'), '6': Value('int64'), '7': Value('int64'), '8': Value('int64'), '9': Value('int64'), '10': Value('int64'), '11': Value('int64'), '12': Value('int64'), '13': Value('int64'), '14': Value('int64')}, 'ret0': {'0': Value('int64'), '1': Value('int64'), '2': Value('int64'), '3': Value('int64'), '4': Value('int64'), '5': Value('int64'), '6': Value('int64'), '7': Value('int64'), '8': Value('int64'), '9': Value('int64'), '10': Value('int64'), '11': Value('int64'), '12': Value('int64'), '13': Value('int64'), '14': Value('int64')}, 'm0_seedA': {'0': Value('int64'), '1': Value('int64'), '2': Value('int64'), '3': Value('int64'), '4': Value('int64'), '5': Value('int64'), '6': Value('int64'), '7': Value('int64'), '8': Value('int64'), '9': Value('int64'), '10': Value('int64'), '11': Value('int64'), '12': Value('int64'), '13': Value('int64'), '14': Value('int64')}, 'm1_seedA': {'0': Value('int64'), '1': Value('int64'), '2': Value('int64'), '3': Value('int64'), '4': Value('int64'), '5': Value('int64'), '6': Value('int64'), '7': Value('int64'), '8': Value('int64'), '9': Value('int64'), '10': Value('int64'), '11': Value('int64'), '12': Value('int64'), '13': Value('int64'), '14': Value('int64')}}, 'files': {'hierarchy.bin': {'bytes': Value('int64'), 'sha256': Value('string')}, 'metadata.json': {'bytes': Value('int64'), 'sha256': Value('string')}, 'octree.bin': {'bytes': Value('int64'), 'sha256': Value('string')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
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 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
version: string
name: string
description: string
points: int64
projection: string
hierarchy: struct<firstChunkSize: int64, stepSize: int64, depth: int64>
child 0, firstChunkSize: int64
child 1, stepSize: int64
child 2, depth: int64
offset: list<item: int64>
child 0, item: int64
scale: list<item: double>
child 0, item: double
spacing: double
boundingBox: struct<min: list<item: int64>, max: list<item: double>>
child 0, min: list<item: int64>
child 0, item: int64
child 1, max: list<item: double>
child 0, item: double
encoding: string
attributes: list<item: struct<name: string, description: string, size: int64, numElements: int64, elementSize: i (... 152 chars omitted)
child 0, item: struct<name: string, description: string, size: int64, numElements: int64, elementSize: int64, type: (... 140 chars omitted)
child 0, name: string
child 1, description: string
child 2, size: int64
child 3, numElements: int64
child 4, elementSize: int64
child 5, type: string
child 6, min: list<item: int64>
child 0, item: int64
child 7, max: list<item: double>
child 0, item: double
child 8, scale: list<item: int64>
child 0, item: int64
child 9, offset: list<item: int64>
child 0, item: int64
child 10, histogram: list<item: int64>
child 0, item: int64
variants: list<item: string>
child 0, item: string
classes_present: struct<returns_ood: struct<0: int64, 1: int
...
, 11: int64
child 12, 12: int64
child 13, 13: int64
child 14, 14: int64
child 3, m1_seedA: struct<0: int64, 1: int64, 2: int64, 3: int64, 4: int64, 5: int64, 6: int64, 7: int64, 8: int64, 9: (... 61 chars omitted)
child 0, 0: int64
child 1, 1: int64
child 2, 2: int64
child 3, 3: int64
child 4, 4: int64
child 5, 5: int64
child 6, 6: int64
child 7, 7: int64
child 8, 8: int64
child 9, 9: int64
child 10, 10: int64
child 11, 11: int64
child 12, 12: int64
child 13, 13: int64
child 14, 14: int64
files: struct<hierarchy.bin: struct<bytes: int64, sha256: string>, metadata.json: struct<bytes: int64, sha2 (... 62 chars omitted)
child 0, hierarchy.bin: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 1, metadata.json: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 2, octree.bin: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
dales_labels_included: bool
city: string
point_source: string
slots: list<item: struct<variant: string, fields: list<item: string>, packing: string>>
child 0, item: struct<variant: string, fields: list<item: string>, packing: string>
child 0, variant: string
child 1, fields: list<item: string>
child 0, item: string
child 2, packing: string
colour_source: null
area: string
to
{'area': Value('string'), 'points': Value('int64'), 'variants': List(Value('string')), 'encoding': Value('string'), 'city': Value('string'), 'point_source': Value('string'), 'colour_source': Value('null'), 'dales_labels_included': Value('bool'), 'slots': List({'variant': Value('string'), 'fields': List(Value('string')), 'packing': Value('string')}), 'attributes': List(Value('string')), 'classes_present': {'returns_ood': {'0': Value('int64'), '1': Value('int64'), '2': Value('int64'), '3': Value('int64'), '4': Value('int64'), '5': Value('int64'), '6': Value('int64'), '7': Value('int64'), '8': Value('int64'), '9': Value('int64'), '10': Value('int64'), '11': Value('int64'), '12': Value('int64'), '13': Value('int64'), '14': Value('int64')}, 'ret0': {'0': Value('int64'), '1': Value('int64'), '2': Value('int64'), '3': Value('int64'), '4': Value('int64'), '5': Value('int64'), '6': Value('int64'), '7': Value('int64'), '8': Value('int64'), '9': Value('int64'), '10': Value('int64'), '11': Value('int64'), '12': Value('int64'), '13': Value('int64'), '14': Value('int64')}, 'm0_seedA': {'0': Value('int64'), '1': Value('int64'), '2': Value('int64'), '3': Value('int64'), '4': Value('int64'), '5': Value('int64'), '6': Value('int64'), '7': Value('int64'), '8': Value('int64'), '9': Value('int64'), '10': Value('int64'), '11': Value('int64'), '12': Value('int64'), '13': Value('int64'), '14': Value('int64')}, 'm1_seedA': {'0': Value('int64'), '1': Value('int64'), '2': Value('int64'), '3': Value('int64'), '4': Value('int64'), '5': Value('int64'), '6': Value('int64'), '7': Value('int64'), '8': Value('int64'), '9': Value('int64'), '10': Value('int64'), '11': Value('int64'), '12': Value('int64'), '13': Value('int64'), '14': Value('int64')}}, 'files': {'hierarchy.bin': {'bytes': Value('int64'), 'sha256': Value('string')}, 'metadata.json': {'bytes': Value('int64'), 'sha256': Value('string')}, 'octree.bin': {'bytes': Value('int64'), 'sha256': Value('string')}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Viewer data: San Francisco and one Surrey tile as octrees
These files are what a static web page streams by HTTP byte range to draw lidar points in 3D.
Each area is one folder holding a PotreeConverter 2.1.3 octree with Brotli encoding
(metadata.json, hierarchy.bin, octree.bin) and a manifest.json with the SHA-256 of every
file, the attributes, the variants and where the points came from.
They are built locally by scripts/pipeline/build_octree.py in the project repository from public
sources and saved predictions, so a clone never depends on this copy.
What is in a point
- position, metres, UTM zone 10N for San Francisco
- colour: NAIP 2022 at the point, San Francisco only
- for each of up to four model variants, the predicted class and the confidence of that
prediction, packed into standard LAS fields (the manifest's
slotsnames them)
The colours a page draws from the class and confidence are predictions of a model trained on lidar from Surrey, British Columbia. They are not measurements and not ground truth.
Licence, per file
| Files | Content | Terms |
|---|---|---|
| San Francisco folders: positions | USGS 3DEP delivery CA_SanFrancisco_1_B23 |
United States Government public domain |
| San Francisco folders: colour | NAIP 2022 imagery, USDA Farm Production and Conservation | United States Government public domain |
| Every folder: class and confidence | predictions made by this project's models | CC BY-NC 4.0, non-commercial, in LICENSE_MODEL.md beside this card |
| Surrey folder: positions | held-out tile of DALES-2 (mbendjilali/DALES-2 on Hugging Face), lidar flown over Surrey | Open Government Licence, City of Surrey: "Contains information licensed under the Open Government License – City of Surrey." |
The Surrey folder holds points and predictions only. It contains no DALES annotation and no mbendjilali/DALES-2 label:
the build never reads the label array, and scripts/pipeline/check_octree_decode.py confirms the class
field equals the model's prediction on every decoded point. The upstream DALES annotations carry
"Creative Commons 3.0" with a separate non-commercial restriction, which is why they are not here.
The predictions, and the model weights behind them, have their own licence, separate from the
MIT licence of the project's code: CC BY-NC 4.0, set out in LICENSE_MODEL.md in this repository.
The reason is upstream. The models were trained on mbendjilali/DALES-2 labels, whose DALES layer
carries that non-commercial restriction; the two older models, M0prime and M1, also on swissSURFACE3D (source: Federal
Office of Topography swisstopo, ©swisstopo), with the BC Freshwater Atlas used to leave DALES water
out of their training:
Contains information licensed under the Open Government Licence – British Columbia.
Commercial use of the predictions needs the upstream restriction settled first, as
LICENSE_MODEL.md says. The project's THIRD_PARTY_NOTICES.md sets out every layer.
No NOAA data is stored here. The water null is not a do-nothing baseline: it knows each point's absolute height in NAVD88 and the sea level at the moment of flight. The model knows neither; it sees only where points lie relative to each other. The sea level used by the project's water null comes from NOAA CO-OPS station 9414290 and is credited on the page that uses it.
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