The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: UnidentifiedImageError
Message: cannot identify image file <_io.BytesIO object at 0x7f4ee52ac040>
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 0x7f4ee52ac040>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.
Sentinel-2 Dataset – New Delhi Region
Dataset Description
This dataset comprises multispectral satellite imagery acquired by the mission over the metropolitan region. Sentinel-2 is part of the Copernicus Programme of the European Space Agency (ESA) and is designed for high-resolution monitoring of land surfaces, urban areas, and environmental processes.
The dataset is intended for geospatial analysis tasks such as image segmentation, feature extraction, and bi-temporal change detection in dense urban and peri-urban environments.
Spatial Coverage
The dataset covers New Delhi and its surrounding areas, capturing a diverse range of land-cover types including dense built-up regions, transportation networks, vegetation, water bodies, and agricultural land. This spatial diversity makes the dataset suitable for urban mapping and environmental monitoring applications.
Spectral Characteristics
Sentinel-2 provides imagery across 13 spectral bands in the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) regions. The bands are available at spatial resolutions of 10 m, 20 m, and 60 m, allowing both fine-scale urban analysis and broader environmental assessments.
Key spectral capabilities include:
- High-resolution visible and NIR bands for urban and vegetation analysis
- SWIR bands for moisture, soil, and built-up area characterization
- Atmospheric bands supporting radiometric correction
Temporal Resolution
The dataset benefits from Sentinel-2’s high revisit frequency of approximately 5 days using the combined Sentinel-2A and Sentinel-2B satellites. This enables bi-temporal and multi-temporal analysis for monitoring urban expansion, seasonal vegetation changes, and environmental dynamics.
Data Products
The imagery is typically provided as:
- Level-1C: Top-of-Atmosphere (TOA) reflectance
- Level-2A: Bottom-of-Atmosphere (BOA) surface reflectance
Data are distributed in a tiled format using the UTM/WGS-84 coordinate reference system and are compatible with common geospatial and remote sensing software tools.
Intended Use
This dataset is suitable for:
- Image segmentation of urban and natural features
- Feature extraction for geospatial machine learning models
- Land-use and land-cover (LULC) classification
- Bi-temporal change detection
- Urban growth and environmental monitoring
License
This dataset is released under the MIT License, allowing free use, modification, and distribution with attribution.
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