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Datasets:
isp-uv-es
/
spacenet6-taco

Tasks:
Image Segmentation
Modalities:
Geospatial
Size:
1K<n<10K
Tags:
earth-observation
remote-sensing
taco
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label spacenet6_test
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 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2474, 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 2391, 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 2192, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1472, 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 1495, 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 1168, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1105, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1126, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label spacenet6_test

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Image and Signal Processing Group, Universitat de València · ELLIOT

SpaceNet 6 Multi-Sensor All-Weather Mapping (MSAW)

This is a repackaging, not a new dataset. It is SpaceNet 6 Multi-Sensor All-Weather Mapping (MSAW) by Capella Space / Metasensing, Maxar, 3DBAG / Kadaster, SpaceNet LLC / CosmiQ Works, converted to TACO. Pixel values and labels are kept as released except where the description below says otherwise. All credit belongs to the original authors: if you use it, please cite them and follow their licence.

original dataset · paper · licence: CC-BY-SA-4.0

Repackaged into TACO by the Image and Signal Processing Group (ISP), Universitat de València, within the ELLIOT project.

Citation

Please cite the original work:

@inproceedings{shermeyer2020spacenet6,
  title     = {SpaceNet 6: Multi-Sensor All Weather Mapping Dataset},
  author    = {Shermeyer, Jacob and Hogan, Daniel and Brown, Jason and Van Etten, Adam and Weir, Nicholas and Pacifici, Fabio and H{\"a}nsch, Ronny and Bastidas, Alexei and Soenen, Scott and Bacastow, Todd and Lewis, Ryan},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  pages     = {768--777},
  year      = {2020},
  doi       = {10.1109/CVPRW50498.2020.00106}
}

About the data

3401 co-registered tiles over Rotterdam. Each pairs quad-polarised airborne X-band SAR (HH, HV, VH, VV) at 0.5 m with pan-sharpened WorldView-2 optical (blue, green, red, NIR) at 0.5 m over the same 450 m square.

3,401 samples · splits: test 919 · train 1,694 · validation 788 · tasks: semantic-segmentation, regression

Packaged as TACO v3.

Full description

Target. 3DBAG building footprints and a per-building roof height.

Geometry. The look angle is extreme, 53-57 degrees off nadir, and half the strips look north, half south.

Splits. The challenge's held-out test tiles ship without labels and are in the held-out half below. The split here is not the publisher's: it is geographic, by easting.

Held-out half

spacenet6_test holds the 2,061 samples whose targets the publisher withheld. Same inputs, no target slots: it is there to be predicted on and submitted, and it belongs in no training mixture.

import os
from huggingface_hub import snapshot_download
from taco.ml import Dataset

root = snapshot_download("isp-uv-es/spacenet6-taco", repo_type="dataset",
                         allow_patterns=["spacenet6_test", "spacenet6_test/*", "spacenet6_test/.tacocat/*"])
ds = Dataset(os.path.join(root, "spacenet6_test"))

Getting started 

git clone --recursive https://github.com/OscarPellicer/taco
pip install -e "taco/python[ml]"     # builds the reader: C++23, CMake, Ninja, pkg-config, libcurl >= 7.83, OpenSSL >= 3

Read it straight from the Hub:

from huggingface_hub import snapshot_download
from taco.ml import Dataset, plot_sample

path = snapshot_download("isp-uv-es/spacenet6-taco", repo_type="dataset", ignore_patterns=["spacenet6_test", "spacenet6_test/*"])
ds = Dataset(path)                  # the repository is the container: parts + .tacocat
plot_sample(ds[0])

or from a local copy:

ds = Dataset("spacenet6")
sample = ds[0]                      # {slot name: SlotValue}, arrays decoded
sample["sar"].array.shape

Metadata without decoding anything:

import taco
taco.read("spacenet6/.tacocat")              # one Arrow table, levels joined

Samples

sample sample sample sample sample

What a sample contains

role slot holds modality detail
input sar raster sar 4 band(s), digital_number
input optical raster optical 4 band(s), digital_number
target buildings mask label_raster 2 classes
target height raster label_raster 1 band(s), unit m, physical

Licence

CC-BY-SA-4.0

Terms of use:

  • Adapted material must be shared under CC-BY-SA-4.0.

Required credits:

  • The SpaceNet Dataset by SpaceNet Partners is licensed under CC-BY-SA-4.0
  • SAR imagery: Capella Space
  • Optical imagery: Maxar WorldView-2
  • Building footprints: 3DBAG (TU Delft 3D geoinformation, Kadaster BAG)

Providers: Capella Space / Metasensing, Maxar, 3DBAG / Kadaster, SpaceNet LLC / CosmiQ Works

Acknowledgements

TACO was designed by César Aybar and is specified at https://asterisk.coop/taco/spec/.

Built by Oscar Pellicer within the ELLIOT project at the Image and Signal Processing Group (ISP), Universitat de València.
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