Datasets:

osmarluiz
/

BSB-Aerial-Dataset

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label BSB-Aerial-Dataset@ce94f2a9d87386e4164eb6e41f955f0add1ab6ba
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 2157, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2152, in encode_example
                  return encode_nested_example(self, example)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1143, in encode_example
                  example_data = self.str2int(example_data)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1080, in str2int
                  output = [self._strval2int(value) for value in values]
                            ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label BSB-Aerial-Dataset@ce94f2a9d87386e4164eb6e41f955f0add1ab6ba

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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

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.py that 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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