eurosat

# Dataset Card for EuroSAT

# Dataset Source

- [Paper with code](https://paperswithcode.com/dataset/eurosat)

# Usage

```python
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:

```bibtex
@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}
}
```

# EuroSAT 数据集卡片
## 数据集来源
- [论文与代码页面](https://paperswithcode.com/dataset/eurosat)

## 使用方法
python
from datasets import load_dataset

dataset = load_dataset('tranganke/eurosat')

### 数据字段
该数据集包含以下字段：
- `image`：RGB 格式图像
- `label`：图像对应的标签，共包含10个类别：
- 0: 一年生作物种植区（annual crop land）
- 1: 森林（forest）
- 2: 灌丛/灌木林地（brushland or shrubland）
- 3: 公路/道路（highway or road）
- 4: 工业建筑/商业建筑（industrial buildings or commercial buildings）
- 5: 牧草地（pasture land）
- 6: 多年生作物种植区（permanent crop land）
- 7: 住宅建筑/民宅/公寓楼（residential buildings or homes or apartments）
- 8: 河流（river）
- 9: 湖泊/海域（lake or sea）

### 数据划分
该数据集包含以下划分子集：
- `train`（训练集）：21600个样本
- `test`（测试集）：2700个样本
- `contrast`（对比集）：2700个样本
- `gaussian_noise`（高斯噪声子集）：2700个样本
- `impulse_noise`（脉冲噪声子集）：2700个样本
- `jpeg_compression`（JPEG 压缩子集）：2700个样本
- `motion_blur`（运动模糊子集）：2700个样本
- `pixelate`（像素化子集）：2700个样本
- `spatter`（喷溅失真子集）：2700个样本

您可使用以下任意一条BibTeX条目加入参考文献列表：
bibtex
@article{helber2019eurosat,
title={EuroSAT：面向土地利用与土地覆盖分类的新型数据集与深度学习基准（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：面向多任务学习的自适应模型融合（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={基于 Concrete 子空间学习的多任务模型融合干扰消除（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={基于专家权重集成混合的多任务模型融合（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}
}

搜集汇总

背景与挑战

背景概述

EuroSAT是一个用于土地利用与土地覆盖分类的遥感图像数据集，包含10个类别，如森林、农田和建筑等。数据以RGB图像格式提供，分为训练集和多个测试集，总计约27,000个样本，适用于多任务学习研究。