Datasets:

alessiopierdominici
/

osm-clip-dataset

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 82, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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🛰️ OpenStreetCLIP Dataset

This dataset contains satellite imagery aligned with OpenStreetMap (OSM) vector metadata, designed for training and evaluating vision-language models with patch-level semantic supervision.

📊 Dataset Structure

The dataset is organized into sharded TAR archives to ensure efficient streaming and bypass API rate limits. All data is located in the data/ directory:

data/images_*.tar: High-resolution satellite images (600x600 pixels).
data/masks.tar: Pre-computed masks for a 7x7 grid. Note that images are resized to 224x224 by the CLIP ViT-B/32 model, resulting in patches of 32x32 pixels.
data/metadata.tar: Filtered OSM JSON files containing the original vector features and tags.

Total samples: 265,135.

⚖️ License & Attribution

This dataset is distributed under the Open Data Commons Open Database License (ODbL).

Attribution: Contains information from OpenStreetMap, which is made available here under the Open Database License (ODbL). © OpenStreetMap contributors.

🚀 How to use

You can load this dataset directly using the Hugging Face datasets library. The library will automatically handle the TAR archives and can stream the data without downloading everything at once.

from datasets import load_dataset

# Load the dataset using streaming (recommended for 100GB+)
dataset = load_dataset(
    "alessiopierdominici/osm-clip-dataset", 
    data_files={
        "train": "data/images_*.tar",
        "masks": "data/masks.tar",
        "metadata": "data/metadata.tar"
    },
    streaming=True
)

# Iterate through the dataset
for sample in dataset["train"]:
    image = sample["image"]
    # Your processing logic here
    break

📖 Citation

Paper currently under review. Citation details will be updated upon publication.

@article{openstreetclip2026,
  title={OpenStreetCLIP: Enhancing Remote Sensing Image-Text Representation Learning with OpenStreetMap Data},
  author={Pierdominici, Alessio and others},
  journal={arXiv preprint (forthcoming)},
  year={2026}
}

🔗 Links

Main Model: alessiopierdominici/osmclip_vit32base
GitHub Repository: alessiopierdominici/OpenStreetCLIP

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Total file size:

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