Cropland Mapping using Geospatial Embeddings
Paper • 2511.02923 • Published
Search is not available for this dataset
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
- Optical imagery (Sentinel-2): https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED
- Radar imagery (Sentinel-1): https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD
- Climatology data (ERA5): https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_AGGR
- Elevation data (SRTM): https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003
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