Datasets:
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/harbor/harbor.py", line 171, in _split_generators
raise DataFilesNotFoundError("No task.toml or instruction.md files found")
datasets.exceptions.DataFilesNotFoundError: No task.toml or instruction.md files found
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.
HLS-LST-Pairs (HLP)
242,416 HLS/fine LST patch pairs across 3,094 MGRS tiles.
Release packaging: lossless-hub-v1-20261005. Release completeness is recorded in release.json.
This is an archival, lossless release with an explicit Python reader; automatic Dataset Viewer support is not claimed.
Data and splits
The original ZIP64 archive is retained byte for byte, including its EOZ/Zstandard shards. Use the separately delivered v2 indices and train-only normalization. The archive embeds historical v1 metadata: its source val denotes the fixed test set. This project constructed the pairs; the legacy reader ID does not mean the data were published by the Prithvi team or generated by a Prithvi model. No measured coarse LST is supplied.
| API split | HLS / records | fine LST samples |
|---|---|---|
| train | 218,975 | 218,975 |
| val | 11,684 | 11,684 |
| test | 11,757 | 11,757 |
Train, val and test membership is fixed. MGRS tile IDs are disjoint within this dataset; this is not proof of zero geographical-footprint overlap. HLP and HLC share some tile IDs and are not automatically independent from one another.
Download and read
hf download dsaaf/HLS-LST-Pairs --repo-type dataset --local-dir ./HLS-LST-Pairs
cd HLS-LST-Pairs
pip install -r eo_data/requirements.txt
python scripts/restore.py
Private repositories require authentication. HLC restoration requires additional space for the extracted files (about 404 GiB); keep the downloaded shards until verification completes. HLP is read directly from its original ZIP.
from pathlib import Path
from eo_data import open_dataset
root = Path('.').resolve()
ds = open_dataset('prithvi', 'lst', 'val', root=root,
prepared=root / 'processed_data/hlp_split_v2_20260914')
sample = ds[0]
temperature_c = ds.denormalize(sample['image']) / 100.0
valid = sample['reference_mask']
ds.close()
LST is stored in Celsius times 100. Apply the valid mask. Normalized images must first be denormalized. Fine LST is a satellite-retrieved reference product. Six HLS channels are Blue, Green, Red, NIR, SWIR1, SWIR2.
The frozen reader exposes individual modalities for reconstruction; it is not a ready-made coarse LST + HLS to fine LST training interface. Retain masks and pairing metadata when constructing paired tasks.
Files and reproducibility
DATASHEET.md: links to the current evidence and detailed data report.processed_data/: frozen indices, normalization and fingerprints, with original paths preserved.eo_data/: the compatible reader.checksums/andrelease.json: file integrity and packaging manifests.scripts/restore.py: verified restoration after download.
The original layout is retained inside this repository so the frozen readers can resolve their paths. Never run preprocessing to replace the supplied splits. HLP v2 must always be selected explicitly.
Provenance, limitations and licensing
See the full current report for sources, dates, masks and limitations. HLP pairs use HLSL30 v2 and Landsat Collection 2 surface temperature; HLC additionally pairs MODIS and Landsat LST. Optical conditions are not guaranteed to be synchronous with each temperature reference.
Dataset-specific author attribution, license and citation are not yet finalized in the source project. No license is inferred from TIDE or from a model name. No DOI or paper citation is invented for this release. The full report discusses remaining provenance and registration checks.
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