The dataset viewer is not available for this subset.
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.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
📖 Overview
This dataset uses Synthetic Aperture Radar (SAR) images from the European Space Agency's (ESA) Copernicus Sentinel-1 mission, which come from two satellites (Sentinel-1A and Sentinel-1B) in polar orbit that operate in all weather conditions, both day and night. The selected Level 1 SAR products are Ground Range Detection (GRD) in wide interferometric mode (IW) and Single Look Complex (SLC) burst products.
The two available polarisation channels (VV and VH) have been exploited from these data. In this case, the VV polarization is useful for detecting flooding in open water areas. Water acts as a smooth surface, causing specular reflection (similar to a mirror), resulting in a very low backscatter signal. Therefore, flooded areas appear as dark areas in the image. By the same token, VH polarisation is particularly sensitive to double bounce scattering, which occurs when the radar signal first bounces off a vertical structure (such as a tree trunk) and then off a horizontal surface (water). This phenomenon is very characteristic of flooded vegetation, causing these areas to appear bright in the image. VH polarisation is less sensitive to the roughness of the water surface. An important new feature of this dataset is the availability of Level 0 data (L0, RAW data) extracted from the Level 1 data (L1) used.
The dataset generated for the Flood Detection case study consists of a total of 6 different SAR scenes. Each scene is composed of a time series. Within each time series, there are different SAR products Sentinel-1 Level 1 Ground Range Detected (GRD) and Single Look Complex (SLC). For the selection of the SAR scenes that will make up the dataset corresponding to the Flood Detection use case, the proposed Kuro Siwo database has been used as the main source. This database offers a manually labelled multi-temporal dataset comprising 43 flood events across different regions of the world. Kuro Siwo is built upon SAR Ground Range Detected products, along with a primary SAR Single Look Complex product that has undergone minimal pre-processing. It is specifically designed to support research focused on leveraging both phase and amplitude information, while also providing maximum flexibility for custom pre-processing in downstream applications.
The labelling strategy is based on XML (Extensible Markup Language) format.
🗂️ Dataset Organisation
The dataset is divided into training, validation, and test folders.
For each patch, the dataset provides:
- 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 containing patch and scene information, water body polygons, and additional information under /labels/xml_labelling
- Binary water body masks under /labels/binary_masks
⬇️ Downloading Data
To download datasets using the Hugging Face Hub:
pip install -U huggingface_hub
huggingface-cli login
Example:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="opensar-insight/flood-detection-dataset",
repo_type="dataset",
local_dir="./data"
)
📚 Resources
For more information, please refer to our website, which will be published soon.
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