The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: UnidentifiedImageError
Message: cannot identify image file <_io.BytesIO object at 0x7f21fc269c20>
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 197, 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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2097, in __iter__
example = _apply_feature_types_on_example(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1635, in _apply_feature_types_on_example
decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2044, in decode_example
return {
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2045, in <dictcomp>
column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1405, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 185, in decode_example
image = PIL.Image.open(bytes_)
File "/src/services/worker/.venv/lib/python3.9/site-packages/PIL/Image.py", line 3339, in open
raise UnidentifiedImageError(msg)
PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7f21fc269c20>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.
POI-based land use classification
Dataset Details
POI-based land use datasets generated and shared by the [Geospatial Science and Human Security Division in Oak Ridge National Laboratory](https://mapspace.ornl.gov/).
This dataset classifies land use into three classes: residential, non-residential and open space.
The dataset has a spatial resolution of 500 meters and covers all countries and regions of the world except for the US and Greenland.
Dataset Description
- Curated by: Geospatial Science and Human Security Division in Oak Ridge National Laboratory
- License: cc-by-4.0
Uses
Direct Use
urban planning, transportation planning, population modeling, disaster risk assessment
Dataset Structure
This dataset has four bands. The pixel values for residential, non-residential and open space bands are probabilities of the area being the land use class. The 'classification' band classifies each pixel into one of the three land use classes with the highest probability.
Source Data
Global POI data from PlanetSense Program.
Bias, Risks, and Limitations
The POI data are not collected for US and Greenland. As a result, the land use result does not cover these two regions. The training dataset used to train the land use classification model are based on OpenStreetMap land use polygons. Some regions have better training data samples than other regions. As a result, the land use classification model accuracy are not the same across the globe. In the future, we will further improve the both the POI data and training data coverage for regions that have limited coverages.
Citation
APA:
Fan, Junchuan & Thakur, Gautam (2024), Three-class Global POI-based land use map, Dataset, https://doi.org/10.17605/OSF.IO/395ZF
Fan, J., & Thakur, G. (2023). Towards POI-based large-scale land use modeling: spatial scale, semantic granularity and geographic context. International Journal of Digital Earth, 16(1), 430–445.
Thakur, G., & Fan, J. (2021). MapSpace: POI-based Multi-Scale Global Land Use Modeling. GIScience Conference 2021.
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