Dataset Card for EuroSAT

Dataset Source

• Paper with code

Usage

from datasets import load_dataset

dataset = load_dataset('tranganke/eurosat')

Data Fields

The dataset contains the following fields:

• image: An image in RGB format.
• label: The label for the image, which is one of 10 classes:
  • 0: annual crop land
  • 1: forest
  • 2: brushland or shrubland
  • 3: highway or road
  • 4: industrial buildings or commercial buildings
  • 5: pasture land
  • 6: permanent crop land
  • 7: residential buildings or homes or apartments
  • 8: river
  • 9: lake or sea

Data Splits

The dataset contains the following splits:

• train: 21,600 examples
• test: 2,700 examples
• contrast: 2,700 examples
• gaussian_noise: 2,700 example
• impulse_noise: 2,700 examples
• jpeg_compression: 2,700 examples
• motion_blur: 2,700 examples
• pixelate: 2,700 examples
• spatter: 2,700 examples

You can use any of the provided BibTeX entries for your reference list:

@article{helber2019eurosat,
  title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
  author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  volume={12},
  number={7},
  pages={2217--2226},
  year={2019},
  publisher={IEEE}
}

@misc{yangAdaMergingAdaptiveModel2023,
  title = {{{AdaMerging}}: {{Adaptive Model Merging}} for {{Multi-Task Learning}}},
  shorttitle = {{{AdaMerging}}},
  author = {Yang, Enneng and Wang, Zhenyi and Shen, Li and Liu, Shiwei and Guo, Guibing and Wang, Xingwei and Tao, Dacheng},
  year = {2023},
  month = oct,
  number = {arXiv:2310.02575},
  eprint = {2310.02575},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2310.02575},
  url = {http://arxiv.org/abs/2310.02575},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning}
}

@misc{tangConcreteSubspaceLearning2023,
  title = {Concrete {{Subspace Learning}} Based {{Interference Elimination}} for {{Multi-task Model Fusion}}},
  author = {Tang, Anke and Shen, Li and Luo, Yong and Ding, Liang and Hu, Han and Du, Bo and Tao, Dacheng},
  year = {2023},
  month = dec,
  number = {arXiv:2312.06173},
  eprint = {2312.06173},
  publisher = {arXiv},
  url = {http://arxiv.org/abs/2312.06173},
  archiveprefix = {arxiv},
  copyright = {All rights reserved},
  keywords = {Computer Science - Machine Learning}
}


@misc{tangMergingMultiTaskModels2024,
  title = {Merging {{Multi-Task Models}} via {{Weight-Ensembling Mixture}} of {{Experts}}},
  author = {Tang, Anke and Shen, Li and Luo, Yong and Yin, Nan and Zhang, Lefei and Tao, Dacheng},
  year = {2024},
  month = feb,
  number = {arXiv:2402.00433},
  eprint = {2402.00433},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2402.00433},
  url = {http://arxiv.org/abs/2402.00433},
  archiveprefix = {arxiv},
  copyright = {All rights reserved},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning}
}