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.
Vessel Detection Dataset
Synthetic Aperture Radar (SAR) dataset for vessel detection, including dark/non-cooperative vessels not broadcasting AIS, 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/dark_vessel.html
- Companion model: opensar-insight/vessel-detection-model
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 available polarisation channels are used. VH (vertical-horizontal) polarisation is generally more conducive to ship detection, since it provides greater contrast between vessels and marine clutter, while VV (vertical-vertical) polarisation carries more information about sea-surface characteristics. A distinguishing feature of this dataset is the inclusion of Level 0 (L0, RAW) data extracted from the corresponding Level 1 products.
The dataset covers 15 SAR scenes. For each Level 1 product (GRD/SLC), the corresponding Level 2 Ocean (OCN) product was retrieved to add wind-condition information, complementing AIS ground truth from the xView3/SARFish reference database.
All L1 products were partitioned into non-overlapping 512x512 pixel blocks. Corresponding patches were extracted from GRD, SLC, L0 RAW, and L0 range-compressed data over the same geographic area, giving four co-registered patch types per block. 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 vessel labels under
/labels
Manual review for higher-quality subset
Some labels are imperfect — offset from the vessel centre, or too small to fully encompass it. A manual review was performed to filter for the highest-quality labels; this filtered subset is listed in yolo_dataset_filtered.csv and was used to train the models in opensar-insight/vessel-detection-model.
Downloading the data
pip install -U huggingface_hub
huggingface-cli login
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="opensar-insight/vessel-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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