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: UnidentifiedImageError
Message: cannot identify image file <_io.BytesIO object at 0x7fef9e693060>
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 190, in decode_example
image = PIL.Image.open(bytes_)
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/PIL/Image.py", line 3498, in open
raise UnidentifiedImageError(msg)
PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7fef9e693060>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.
Dataset Card for ShadeBench
ShadeBench is a large-scale multimodal dataset for urban shade simulation and understanding, supporting tasks such as generation, segmentation, and 3D reconstruction.
Originally introduced in the DeepShade project (IJCAI 2025), ShadeBench is an extended benchmark submitted to KDD 2026 (AI4Science Track), with improved spatial alignment, physically grounded solar modeling, and additional 3D geometry.
Dataset Details
- Cities: 34 (across 6 continents)
- Total Files: ~137,000
- Resolution: 1024 × 1024
- Modalities: Image, Text, Time, 3D Geometry
Each city contains:
- source/ and target/ image pairs (shade transitions)
- train.json and test.json (with prompts and paths)
- Satellite images (real-world context)
- Masked images (buildings aligned with simulation)
- obj_grids/ (3D building meshes in .obj format)
Key Features
- Spatially aligned data between satellite imagery and simulated building layouts
- Physically consistent shadows using solar position modeling
- Temporal variation across different times of day
- Multimodal learning support (image + text + geometry)
Use Cases
- Text-conditioned image generation
- Shadow simulation and prediction
- Shade segmentation
- 3D urban reconstruction
- GIS + ML research
Summmary
ShadeBench extends DeepShade into a benchmark dataset by adding aligned satellite data, masked building regions, and 3D geometry, enabling realistic and scalable modeling of urban shade.
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