Datasets:

lammtfkday
/

deepglobe-roads

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label deepglobe-roads@fe078d210aa88cbd9c355b72dd88016c32162a21
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2386, 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 2303, 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 2178, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1460, 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 1483, 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 1158, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1095, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1116, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label deepglobe-roads@fe078d210aa88cbd9c355b72dd88016c32162a21

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DeepGlobe Road Extraction Dataset (Partitioned Benchmark)

Dataset Description

The DeepGlobe Road Extraction Dataset consists of 6,226 optical satellite images with a spatial dimension of $1024 \times 1024$ pixels and a Ground Sampling Distance (GSD) of 0.5 m/pixel. The imagery spans varied urban, suburban, and rural terrains across Thailand, Indonesia, and India.

This repository hosts the standardized, reproducible benchmark partition:

Training Set: 5,000 image/mask pairs
Validation Set: 600 image/mask pairs (held-out for validation and hyperparameter selection)
Test Set: 626 image/mask pairs (unseen benchmark evaluation partition)
Total: 6,226 optical satellite image pairs ($1024 \times 1024$ pixels)

Partitioning is constructed deterministically with seed 42 from the sorted filename collection.

Structure & Files

├── README.md               # Dataset documentation card
├── metadata.csv            # Manifest with columns: split, tile_id, image_filename, mask_filename, width, height, gsd_m
├── splits.json             # Structured JSON partition mapping for programmatic loaders
├── train.zip               # 5,000 training pairs (images/ and masks/)
├── val.zip                 # 600 validation pairs (images/ and masks/)
├── test.zip                # 626 test pairs (images/ and masks/)
└── samples/                # Representative high-resolution visual previews

Data Format

Images: Optical 3-channel RGB (*_sat.jpg), $1024 \times 1024$ pixels, 0.5 m/pixel GSD.
Masks: Binary road masks (*_mask.png), $1024 \times 1024$ pixels. Pixel value 255 (or $>127$) denotes road pixels, 0 denotes background/non-road.

Python Usage Example

from huggingface_hub import hf_hub_download
import zipfile

# Download test split
test_zip = hf_hub_download(repo_id="lammtfkday/deepglobe-roads", filename="test.zip", repo_type="dataset")
with zipfile.ZipFile(test_zip, "r") as zf:
    zf.extractall("./data/deepglobe")

print("DeepGlobe test split extracted successfully.")

Citation

If you use this dataset in your research, please cite the original challenge paper:

@inproceedings{demir2018deepglobe,
  title={DeepGlobe 2018: A challenge to parse the earth through satellite images},
  author={Demir, Ilke and Koperski, Krzysztof and Lindenbaum, David and Pang, Guan and Huang, Jing and Basu, Saikat and Hughes, Forest and Tuia, Devis and Kumar, Ramesh},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  pages={172--181},
  year={2018}
}

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