Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: OSError
Message: -2
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
example = _apply_feature_types_on_example(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2159, in _apply_feature_types_on_example
decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2204, in decode_example
column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1508, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 193, in decode_example
image.load() # to avoid "Too many open files" errors
^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/PIL/TiffImagePlugin.py", line 1237, in load
return self._load_libtiff()
^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/PIL/TiffImagePlugin.py", line 1342, in _load_libtiff
raise OSError(err)
OSError: -2Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
HighBuild-1M
HighBuild-1M is a multi-continental high-resolution benchmark dataset for single-view building height estimation from overhead imagery. Each sample contains a 1024 x 1024 RGB overhead image, a spatially aligned float32 building-height mask, and COCO-style building instance annotations.
Dataset Statistics
| Version | 1024 x 1024 tiles | Building instances | City groups | Countries/regions | Continents |
|---|---|---|---|---|---|
| HighBuild-1M full benchmark | 70,266 | 6,050,823 | 26 | 12 | 6 |
The full benchmark covers 6 continents, 12 countries or regions, and 26 city groups, with 70,266 paired 1024 x 1024 tiles and 6,050,823 building instances.
Reviewer Small Sample
A small reviewer-inspection subset is available at:
https://huggingface.co/datasets/feifei140729/small-sample
The small sample follows the same triplet structure as the full dataset, including RGB images, float32 TIFF building-height masks, and COCO-style JSON annotations. It is intended for quick inspection of data quality, file organization, spatial alignment, and annotation format.
Dataset Viewer Note
The Hugging Face Dataset Viewer may fail to render this dataset because HighBuild-1M is distributed as large WebDataset TAR shards containing JPEG images, float32 TIFF masks, and COCO-style JSON annotations rather than a single tabular dataset. This does not affect downloading, streaming, or inspecting the dataset. Please use the WebDataset loading instructions and manifest files below.
Tasks
HighBuild-1M supports the following tasks:
Single-view building height estimation
Input: one 1024 x 1024 RGB overhead image tile.
Output: a spatially aligned float32 building-height map.Building-wise height evaluation
Predicted height maps can be aggregated within COCO-style building polygons to compute building-level MAE/RMSE.Building segmentation and instance-level building understanding
COCO-style building polygons and bounding boxes can be used for semantic building segmentation, instance-level building analysis, and joint height-segmentation modelling.Spatial generalization benchmarking
The benchmark supports same-city, cross-city within-country, and cross-country evaluation protocols.
WebDataset and Compression
For the Hugging Face hosted release, the recommended distribution format is WebDataset TAR shards.
The hosted WebDataset release uses split-aware shards under:
data/webdataset/train/*.tar
data/webdataset/validation/*.tar
data/webdataset/test/*.tar
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