Datasets:
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Cannot get the split names for the config 'default' of the dataset.
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 246, 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 66, 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.
A Globally Balanced Wildfire Satellite Dataset for VLM Bias Evaluation
Overview
This is a geographically balanced dataset of wildfire events captured by Sentinel-2 satellite imagery, designed to benchmark vision-language models (VLMs) on wildfire detection and burn severity estimation across diverse global regions.
Each sample consists of a paired pre-fire and post-fire RGB chip (224×224 px, 10 m GSD) with continuous dNBR ground truth derived from Sentinel-2 NIR/SWIR bands. The dataset is balanced across six continents and five ecosystem types to enable controlled analysis of geographic performance disparities in VLMs.
Dataset Structure
Each example contains:
| Field | Description |
|---|---|
pre_image_path |
Pre-fire Sentinel-2 true-color RGB PNG |
post_image_path |
Post-fire Sentinel-2 true-color RGB PNG |
dnbr_image_path |
dNBR severity map (grayscale PNG) |
kind |
burn or neg (paired no-fire control) |
severity3_label |
Ordinal severity: 0=low, 1=moderate, 2=high |
dnbr_mean |
Continuous mean dNBR value (ground truth) |
continent |
One of six continents |
country |
Country of the event |
ecosystem_group |
forest, shrubland_chaparral, grassland_savanna, cropland, wetland |
latitude_band |
tropical, subtropical, temperate, boreal_polar |
split |
train / val / test (70/15/15, event-level) |
GeoTIFF versions of all images are also provided.
Key Design Choices
- Continental balance: Equal target count per continent to avoid geographic skew.
- Ecosystem stratification: Samples are distributed across ecosystem types within each continent so geographic and biome effects can be disentangled.
- Paired burn/negative: Each burn event has a matched no-fire control chip from a nearby unburned location, verified fire-free via MODIS and Sentinel-2 dNBR.
- Tight temporal window: Post-fire images are captured 5–25 days after the burn event to minimize seasonal confounds.
- Quality filters: Minimum dNBR threshold (0.15) and burn pixel fraction (10%) ensure burns are visually distinguishable in RGB. Negatives must have dNBR ≤ 0.05.
Data Sources
- Burn detection: MODIS MCD64A1 Collection 6 (500 m monthly burned area)
- Imagery: Sentinel-2 L2A via Microsoft Planetary Computer
- Land cover: ESA WorldCover 10 m
- Boundaries: Natural Earth 110 m
Intended Use
This dataset is intended for:
- Benchmarking VLMs on wildfire detection and severity estimation from satellite RGB imagery
- Evaluating geographic and ecosystem-level performance disparities
- Studying the limitations of general-purpose VLMs on remote sensing tasks
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