Hugging Face's logo

Datasets:
izvonkov
/
Togo_Presto_Embeddings

ArXiv:
License:
Dataset card Files
xet
Community
Dataset Viewer
Duplicate
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:    UnidentifiedImageError
Message:      cannot identify image file <_io.BytesIO object at 0x7f2b4c9d0040>
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 0x7f2b4c9d0040>

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.

Dataset Card for Togo Presto Embeddings

Geospatial embeddings for Togo generated using the Presto geospatial foundation model.

Dataset Details

Presto geospatial embeddings provide a compressed representation of Earth Observation data, enabling more efficient mapping and analysis. Embeddings are generated by using the Presto encoder to compress location information, optical imagery (Sentinel-2), radar imagery (Sentinel-1), climatology data (ERA5), and elevation data (SRTM) over the course of a year (March 2019 - March 2020). Each embedding contains 128 features representing a single 10m2 pixel on Earth. Embeddings can be used in place of raw Earth Observation data for various machine-learning tasks, such as classification, clustering, and anomaly detection.

  • Curated by: Ivan Zvonkov, Gabriel Tseng, Inbal Becker-Reshef, Hannah Kerner
  • License: cc-by-4.0

Dataset Sources

Uses

Geospatial embeddings offer a novel, efficient, and accessible way to map landscape features (such as cropland).

Dataset Structure

The embeddings are represented in a series of geotiff files covering Togo. They are also available as a Google Earth Engine asset: https://code.earthengine.google.com/?asset=users/izvonkov/Togo/Presto_embeddings_v2025_06_19

Dataset Creation

Our embeddings contain modified Copernicus Sentinel-1 and Sentinel-2 data (© ESA), ERA5 data (© ECMWF/Copernicus Climate Change Service), and SRTM DEM data (NASA/USGS).

Source Data

Downloads last month
217

Paper for izvonkov/Togo_Presto_Embeddings