Datasets:
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: ValueError
Message: Invalid string class label BSB-Aerial-Dataset@4c7ca04eeb22baf690f9c13df0235c9d978c31b4
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 2572, in __iter__
example = _apply_feature_types_on_example(
example, self.features, token_per_repo_id=self.token_per_repo_id
)
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2489, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2244, in encode_example
return encode_nested_example(self, example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1518, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1541, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1210, in encode_example
example_data = self.str2int(example_data)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1147, in str2int
output = [self._strval2int(value) for value in values]
~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1168, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label BSB-Aerial-Dataset@4c7ca04eeb22baf690f9c13df0235c9d978c31b4Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
BSB Aerial Dataset
A panoptic segmentation dataset of aerial imagery from Brasilia, Brazil, annotated in COCO format.
Dataset Description
The BSB Aerial Dataset contains 3,400 aerial image tiles (512x512 pixels) of urban areas in Brasilia, annotated for panoptic segmentation with 14 categories covering both "stuff" and "things" classes.
Splits
| Split | Images |
|---|---|
| Train | 3,000 |
| Val | 200 |
| Test | 200 |
Categories
| ID | Name | Type |
|---|---|---|
| 1 | Street | Stuff |
| 2 | Permeable Area | Stuff |
| 3 | Lake | Stuff |
| 4 | Swimming Pool | Thing |
| 5 | Harbor | Thing |
| 6 | Vehicle | Thing |
| 7 | Boat | Thing |
| 8 | Sports Court | Thing |
| 9 | Soccer Field | Thing |
| 10 | Comm. Building | Thing |
| 11 | Comm. Building Block | Thing |
| 12 | Res. Building | Thing |
| 13 | House | Thing |
| 14 | Small Construction | Thing |
Dataset Structure
bsb_dataset/
├── annotations/
│ ├── panoptic_train.json
│ ├── panoptic_val.json
│ ├── panoptic_test.json
│ ├── instance_train.json
│ ├── instance_val.json
│ └── instance_test.json
├── image_train/ # RGB aerial tiles (TIFF)
├── image_val/
├── image_test/
├── panoptic_train/ # Panoptic segmentation masks
├── panoptic_val/
├── panoptic_test/
├── panoptic_stuff_train/ # Stuff-only masks
├── panoptic_stuff_val/
├── panoptic_stuff_test/
├── class_train/ # Semantic class masks
├── class_val/
└── class_test/
Annotation Format
Annotations follow the COCO Panoptic format. Each annotation JSON contains image metadata and segment information compatible with tools like Detectron2.
Usage with Detectron2
See the example notebook in the GitHub repository for a full implementation using Detectron2's Panoptic-FPN.
Note: The repository includes a modified
detection_utils.pythat properly handles RGB image tiles. Replace the original Detectron2 file with the provided version.
Related Tools
- Panoptic-Generator — Tool for building remote sensing panoptic segmentation datasets in COCO format using GIS software.
Citation
If you use this dataset in your research, please cite:
@misc{bsb_aerial_dataset,
author = {Osmar Luiz Carvalho},
title = {BSB Aerial Dataset: Panoptic Segmentation of Aerial Imagery from Brasilia},
year = {2024},
url = {https://github.com/osmarluiz/BSB-Aerial-Dataset}
}
License
This dataset is released under the MIT License.
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