metadata

task_categories:
  - image-segmentation

NLCD-L

This dataset incorporates both SSL4EO-L Benchmark dataset and the NLCD-L dataset which is derived from the original SSL4EO-L Benchmark dataset by combining optical data from Landsat-7 and Landsat 8-9 with NLCD ground-truth labels, originally proposed in SSL4EO-L. The dataset contains 20 MSI bands, deliberately exceeding Sentinel-2’s channel count. It comprises 17,500 training samples, 3,750 validation samples, and 3,750 test samples.

Please refer to the original SSL4EO-L paper for more detailed information about the original SSL4EO-L Benchmark dataset:

Paper: https://arxiv.org/abs/2306.09424

How to Use This Dataset

from datasets import load_dataset

# To access NLCD-L, set name to etm_oli_toa_nlcd in load_dataset function
dataset = load_dataset("GFM-Bench/SSL4EO-L-Benchmark", name="etm_oli_toa_nlcd")

Also, please see our GFM-Bench repository for more information about how to use the dataset! 🤗

Dataset Metadata

The following metadata provides details about the Landsat imagery used in the dataset:

Configuration Name	Number of Bands	Number of Label Classes	Spatial Resolution
etm_sr_cdl	6	134	30
etm_sr_nlcd	6	21	30
etm_toa_cdl	9	134	30
etm_toa_nlcd	9	21	30
oli_sr_nlcd	7	134	30
oli_sr_nlcd	7	21	30
oli_tirs_toa_cdl	11	134	30
oli_tirs_toa_nlcd	11	21	30
etm_oli_toa_cdl	20	134	30
etm_oli_toa_nlcd	20	21	30

Dataset Splits

The NLCD-L and SSL4EO-L Benchmark dataset consist following splits:

train: 17,500 samples
val: 3,750 samples
test: 3,750 samples

Dataset Features:

The NLCD-L and SSL4EO-L dataset consist of following features:

optical: the Landsat image.
label: the segmentation labels.
optical_channel_wv: the central wavelength of each Landsat bands.
spatial_resolution: the spatial resolution of images.

Citation

If you use either the NLCD-L dataset or the original SSL4EO-L Benchmark dataset in your work, please cite the original paper:

@article{stewart2023ssl4eo,
  title={Ssl4eo-l: Datasets and foundation models for landsat imagery},
  author={Stewart, Adam and Lehmann, Nils and Corley, Isaac and Wang, Yi and Chang, Yi-Chia and Ait Ali Braham, Nassim Ait and Sehgal, Shradha and Robinson, Caleb and Banerjee, Arindam},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  pages={59787--59807},
  year={2023}
}

and if you also find our benchmark useful, please consider citing our paper:

@misc{si2025scalablefoundationmodelmultimodal,
      title={Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data}, 
      author={Haozhe Si and Yuxuan Wan and Minh Do and Deepak Vasisht and Han Zhao and Hendrik F. Hamann},
      year={2025},
      eprint={2503.12843},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.12843}, 
}