The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 250, in _split_generators
raise ValueError(
"`file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files"
)
ValueError: `file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
About
This is a dataset composed of satellite images of cities from about 1200m high.
It includes satellite imagery, as well as imagery at about the ground level, labeled "ground_truth".
metadata.csv contains mappings between a city's folder name and it's corresponding coordinates, as well as it's country.
Data Collection Procedure
All data collected came from Google Earth Studio. To produce the satellite & ground truth frames, we use the preset “Orbit” project. This can be reached from File -> New -> Quick Starts -> Orbit. From here, you can type in coordinates of the form "x°, y°", where x is the desired latitude in degrees, and y is the desired longitude in degrees. Alternatively, typing in a city name will give default coordinates, which is what was used for most cities in this dataset.
For the satellite image folders, the only parameters touched were Radius and Altitude, whilst the rest used defaults. Radius was set to 688m, and the Altitude was set to approximately 1200 + Target Altitude meters. Approximately is because the options are discrete fixed values – the discrete value closest to 1200 from above was chosen. After this, the length is set to 2s. With the default fps of 30, this will produce 61 frames labeled 00-60. The scene is then rendered as an image sequence of .jpeg files, with square dimensions 2048x2048. Under “Advanced,” we used JSON 3D Tracking Data with a Global Coordinate space, Map Style set to Clean, and Texture Quality set to High.
For the ground_truth image folders, a similar process was followed, however the Radius and Altitude parameters were different. These were qualitatively chosen to ensure a “proper” view of the architecture from a side view, clearly displaying the exterior walls of buildings. In general, parameters would be roughly 500 <= Radius <= 800 and Target Altitude + 300 <= Altitude <= Target Altitude + 500. For example, a city with tall buildings like New York, New York might lie in the larger end of those ranges, whilst a more humble city like Burlington, Vermont will hit the bottom end.
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