| --- |
| 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 |
| ```python |
| 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](https://github.com/uiuctml/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: |
| <!--- **radar**: the Sentinel-1 image.--> |
| - **optical**: the Landsat image. |
| - **label**: the segmentation labels. |
| <!--- **radar_channel_wv**: the central wavelength of each Sentinel-1 bands.--> |
| - **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}, |
| } |
| ``` |
