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/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 237, in _split_generators
raise ValueError(
ValueError: `file_name` or `*_file_name` must be present as dictionary key (with type string) 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 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 294, 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.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Synthetic Satellite Land-Use Image Dataset
Dataset Description
This dataset contains synthetic satellite-like RGB image patches generated using a pre-trained Stable Diffusion model from Hugging Face.
The images simulate top-down satellite views of different land-use categories and were created for academic and educational purposes, including exploratory data analysis (EDA) and machine learning experimentation.
Dataset Structure
The dataset is organized as follows:
- images/
- Forest/
- SeaLake/
- River/
- Residential/
- Highway/
- AnnualCrop/
- metadata.csv
Each image is stored in a folder corresponding to its land-use label.
The metadata.csv file contains information about each image, including its label, generation prompt, seed, and model ID.
Exploratory Data Analysis (EDA)
The dataset contains a total of 1,200 images distributed equally across six land-use classes.
Class Distribution
Each class contains exactly 200 images, making the dataset fully balanced.
This balance is ideal for training and evaluating classification models without class bias.
Visual Inspection
A random sample of images from each class was inspected.
The visual patterns of the images are consistent with their labels:
- Forest images show dense green textures
- SeaLake and River images depict water bodies
- Residential images contain structured urban patterns
- Highway images show long linear road structures
- AnnualCrop images display regular agricultural field patterns
Data Generation
All
e synthetically generated using the stabilityai/sd-turbo pre-trained diffusion model from Hugging Face.
The generation process and EDA were performed using Python notebooks, which are included in this repository.
Potential Use Cases
- Image classification experiments
- Computer vision coursework
- Exploratory data analysis demonstrations
- Synthetic data research and prototyping
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