Datasets:

opensar-insight
/

flood-detection-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/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
                  first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
                                                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
                  cls = get_filesystem_class(protocol)
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
                  raise ValueError(f"Protocol not known: {protocol}")
              ValueError: Protocol not known: memory
              
              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 71, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/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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Flood Detection Dataset

Synthetic Aperture Radar (SAR) dataset for flood and water body detection, developed as part of the OpenSAR Insight project. See the organization card for full project background, funding, and consortium details.

Codebase: https://github.com/ESA-PhiLab/OpenSARInsight
Project page: https://opensarinsightweb.web.uah.es/flood_detection.html

Overview

This dataset uses SAR images from the ESA Copernicus Sentinel-1 mission (Sentinel-1A and Sentinel-1B), which operate in all weather conditions, day and night. The selected Level 1 SAR products are Ground Range Detected (GRD) in wide interferometric mode (IW) and Single Look Complex (SLC) burst products.

Both polarisation channels are used. VV polarisation is useful for detecting flooding in open water: water acts as a smooth surface causing specular reflection, so flooded areas appear dark. VH polarisation is particularly sensitive to double-bounce scattering — the radar signal bouncing off a vertical structure (e.g. a tree trunk) and then a horizontal water surface — which is characteristic of flooded vegetation and appears bright. VH is also less sensitive to water-surface roughness than VV. As with the other OpenSAR Insight datasets, Level 0 (L0, RAW) data extracted from the Level 1 products is included.

The dataset covers 6 SAR scenes, each a time series of Sentinel-1 GRD and SLC products. Scene selection was based on the Kuro Siwo database, a manually labelled multi-temporal dataset covering 43 flood events across different regions of the world, built on GRD products alongside a minimally processed SLC product to support research using both phase and amplitude information. Labels are provided in XML format.

Dataset organisation

The dataset is split into training, validation, and test folders. For each patch:

512x512 VV and VH L1 SLC patches under /patches
Corresponding VV and VH L1 GRD patches under /patches
Corresponding VV and VH L0 patches under /raw
Corresponding VV and VH L0 range-compressed patches under /range_compressed_rescaled
XML file with patch/scene information and water body polygons under /labels/xml_labelling
Binary water body masks under /labels/binary_masks

Downloading the data

pip install -U huggingface_hub

huggingface-cli login

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="opensar-insight/flood-detection-dataset",
    repo_type="dataset",
    local_dir="./data"
)

License

MIT License. See the repository LICENSE for details on components of the broader codebase.

Citation

@misc{opensarinsight,
  title        = {OpenSAR Insight: ML-ready datasets and models for direct insight generation from raw SAR data},
  author       = {{Indra Space} and {INTA} and {Universidad de Alcal\'a de Henares}},
  howpublished = {\url{https://github.com/ESA-PhiLab/OpenSARInsight}},
  note         = {Funded by ESA \(\Phi\)-lab}
}

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