Datasets:

dm4eo
/

ssl4eo_eu_forest

Error code:   StreamingRowsError
Exception:    UnidentifiedImageError
Message:      cannot identify image file <_io.BytesIO object at 0x7f7061b5ba10>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2431, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1953, in __iter__
                  batch = formatter.format_batch(pa_table)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 472, in format_batch
                  batch = self.python_features_decoder.decode_batch(batch)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 234, in decode_batch
                  return self.features.decode_batch(batch, token_per_repo_id=self.token_per_repo_id) if self.features else batch
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2147, in decode_batch
                  decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1409, in decode_nested_example
                  return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 190, in decode_example
                  image = PIL.Image.open(bytes_)
                          ^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/PIL/Image.py", line 3498, in open
                  raise UnidentifiedImageError(msg)
              PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7f7061b5ba10>

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SSL4EO-EU-Forest

_{Sentinel-2 winter scene}

_{tree (green) segmentation}

_{Sentinel-2 summer scene}

_{tree (green) segmentation}

Introduction

The dataset is an add-on to the globally sampled SSL4EO-S12 pre-training dataset (https://doi.org/10.1109/MGRS.2023.3281651) where v1.1 is available at https://huggingface.co/datasets/embed2scale/SSL4EO-S12-v1.1 with downstream tasks in https://huggingface.co/datasets/embed2scale/SSL4EO-S12-downstream .

Data Characteristics

SSL4EO-EU-Forest assembles an additional downstream task (85 GB) for binary semantic segmentation of forest land cover as follows:

images/ sub-directory: It curates 16K spatial patches (264x264 pixels) of Sentinel-2 L2A imagery (UInt16 GeoTIFF, 12 bands upsampled to 10m pixel resolution) over European forests. Each geolocation contains up to four Sentinel-2 timestamps from all seasons (winter, spring, summer, fall) of year 2018. The cloud cover in each scene is below 10%.
masks/ sub-directory: Forest masks (0 = non-tree covered areas, 1 = broadleaved forest, 2 = coniferous forest; Byte GeoTIFF) have been derived from the 2018 HRL dataset (https://doi.org/10.2909/486f77da-d605-423e-93a9-680760ab6791) available through the Copernicus Land Monitoring Service, cf. https://land.copernicus.eu/en/products/high-resolution-layer-forests-and-tree-cover/dominant-leaf-type-2018-raster-10-m-europe-yearly .
Data of a geolocation are placed in numbered sub-directories (e.g. 0017618) of images/ and masks/, respectively. Each of those for the images/ directory contains four directories indicating the four seasons as per convention by Sentinel-2. For example, 20180520T094031_20180520T094033_T35VMH denotes the sensing start time as approx. May 20, 2018 at 9.41am UTC (spring scene) for geolocation defined by tile ID T35VMH (which the patch was cropped from). General details on Sentinel-2 products in https://sentiwiki.copernicus.eu/web/s2-products .

Spatial Coverage

Spatial Distribution of SSL4EO-EU-Forest

Access API

see https://github.com/cmalbrec/ssl4eo_eu_forest

Funding

This work was carried under the EvoLand project, cf. https://www.evo-land.eu . This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101082130.

Citation

@misc{ssl4eo_eu_forest,
  author       = {Braham Ait Ali, Nassim and Albrecht, Conrad M},
  title        = {SSL4EO-EU Forest Dataset},
  year         = {2025},
  howpublished = {https://github.com/cmalbrec/ssl4eo_eu_forest},
  doi          = {10.5281/zenodo.17290776}
}

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