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---
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#
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- **图像类型**: 光学图像、SAR图像、二值变化图
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- **文件格式**: TIF(原始图像)、PNG(变化黑白二值图)、JSON(标注文件)
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- **预处理状态**: 已配准对齐裁剪,尚未按固定分辨率分割
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- **标注完整性**: 包含像素级变化检测标注
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- **A/**: 高分二号(Gaofen-2)事前光学图像(.tif)- 变化检测的参考基准图像
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- **B/**: 高分三号(Gaofen-3)事后SAR图像(.tif)- 合成孔径雷达图像
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- **C/**: 哨兵二号(Sentinel-2)未处理的事后光学图像(.tif)- 原始光学图像
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- **D/**: 哨兵二号(Sentinel-2)相对辐射校正后的事后光学图像(.tif)- 经过预处理的光学图像
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- **E/**: 黑白二值像素变化图(.png)- 变化检测
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###
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1. **变化检测算法研究** - 开发和测试新的变化检测方法
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2. **多模态融合** - 研究光学图像与SAR图像的融合技术
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3. **图像预处理评估** - 比较不同预处理方法的效果
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4. **深度学习** - 作为训练和测试数据
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###
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- **通道数**: 3
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2. **预处理需求**: 根据具体应用需求,必须进一步统一分辨率
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3. **去重**:虽然每组图像是人工单独标注的,但是为了避免重叠区域导致的验证集和训练集混淆,可根据坐标自行去重
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```bibtex
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@dataset{remote_sensing_change_detection_2025,
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```
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##
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##
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- Hugging Face: [@Mercyiris](https://huggingface.co/Mercyiris)
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size_categories:
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- n<1K
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language:
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- en
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pretty_name: "Remote Sensing Change Detection Dataset"
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---
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# Remote Sensing Change Detection Dataset
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*For Chinese documentation, please see [README_zh.md](./README_zh.md)*
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## Dataset Description
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A specialized dataset for remote sensing change detection research, containing complete image processing pipeline and annotation information. This dataset includes 24 groups of registered and aligned remote sensing image samples, with each group containing 5 different types of image files and corresponding annotation files.
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## Dataset Features
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- **Data Scale**: 24 groups of image samples
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- **Image Types**: Optical images, SAR images, binary change maps
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- **File Formats**: TIF (original images), PNG (change binary maps), JSON (annotation files)
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- **Preprocessing Status**: Registered, aligned, and cropped, not yet segmented by fixed resolution
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- **Annotation Completeness**: Contains pixel-level change detection annotations
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## File Structure
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The dataset contains the following 6 directories:
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### Image Files
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- **A/**: Gaofen-2 pre-event optical images (.tif) - Reference baseline images for change detection
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- **B/**: Gaofen-3 post-event SAR images (.tif) - Synthetic Aperture Radar images
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- **C/**: Sentinel-2 unprocessed post-event optical images (.tif) - Raw optical images
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- **D/**: Sentinel-2 relatively radiometrically corrected post-event optical images (.tif) - Preprocessed optical images
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- **E/**: Binary change maps (.png) - Change detection results
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### Annotation Files
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- **json/**: JSON annotation files corresponding to change maps, can be read and modified using [LabelmeCD-AI](https://github.com/Mriris/labelme_cd_AI)
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## Dataset Applications
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### Primary Use Cases
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1. **Change Detection Algorithm Research** - Develop and test new change detection methods
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2. **Multimodal Fusion** - Research fusion techniques for optical and SAR images
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3. **Image Preprocessing Evaluation** - Compare effects of different preprocessing methods
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4. **Deep Learning** - Use as training and testing data
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### Research Directions
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- Time-series remote sensing image analysis
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- Multispectral image processing
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- Urban building change monitoring
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## Technical Specifications
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- **Processing Status**: Registered and aligned
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- **Channels**: 3
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## Important Notes
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1. **File Integrity**: Ensure consistent file counts across A, B, C, D, E directories
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2. **Preprocessing Requirements**: Further resolution unification required based on specific application needs
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3. **Deduplication**: Although each group of images is manually annotated separately, to avoid confusion between validation and training sets due to overlapping regions, deduplication can be performed based on coordinates
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## Citation
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If you use this dataset in your research, please cite:
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```bibtex
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@dataset{remote_sensing_change_detection_2025,
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}
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```
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## License
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This dataset is released under the CC BY 4.0 license, allowing free use, modification, and distribution with proper attribution.
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## Contact
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For any questions or suggestions, please contact:
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- Hugging Face: [@Mercyiris](https://huggingface.co/Mercyiris)
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- Email: [email protected]
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README_zh.md
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---
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license: cc-by-4.0
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task_categories:
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- image-segmentation
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- image-to-image
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tags:
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- remote-sensing
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- change-detection
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- optical-images
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- sar-images
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- image-processing
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size_categories:
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- n<1K
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language:
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- en
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- zh
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pretty_name: "遥感图像变化检测数据集"
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---
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# 遥感图像变化检测数据集
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*For English documentation, please see [README.md](./README.md)*
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## 数据集描述
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专门用于遥感图像变化检测研究的数据集,包含了完整的图像处理流程和标注信息。该数据集包含24组配准对齐的遥感图像样本,每组样本包含5种不同类型的图像文件和对应的标注文件。
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## 数据集特点
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- **数据规模**: 24组图像样本
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- **图像类型**: 光学图像、SAR图像、二值变化图
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- **文件格式**: TIF(原始图像)、PNG(变化黑白二值图)、JSON(标注文件)
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- **预处理状态**: 已配准对齐裁剪,尚未按固定分辨率分割
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- **标注完整性**: 包含像素级变化检测标注
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## 文件结构
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数据集包含以下6个目录:
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### 图像文件
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- **A/**: 高分二号(Gaofen-2)事前光学图像(.tif)- 变化检测的参考基准图像
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- **B/**: 高分三号(Gaofen-3)事后SAR图像(.tif)- 合成孔径雷达图像
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- **C/**: 哨兵二号(Sentinel-2)未处理的事后光学图像(.tif)- 原始光学图像
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- **D/**: 哨兵二号(Sentinel-2)相对辐射校正后的事后光学图像(.tif)- 经过预处理的光学图像
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- **E/**: 黑白二值像素变化图(.png)- 变化检测
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### 标注文件
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- **json/**: 变化图对应的JSON标注文件,可以使用[LabelmeCD-AI](https://github.com/Mriris/labelme_cd_AI)读取和修改
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## 数据集用途
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### 主要应用场景
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1. **变化检测算法研究** - 开发和测试新的变化检测方法
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2. **多模态融合** - 研究光学图像与SAR图像的融合技术
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3. **图像预处理评估** - 比较不同预处理方法的效果
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4. **深度学习** - 作为训练和测试数据
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### 研究方向
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- 时序遥感图像分析
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- 多光谱图像处理
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- 城市建筑变化监测
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## 技术规格
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- **处理状态**: 已配准对齐
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- **通道数**: 3
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## 注意事项
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1. **文件完整性**: 确保A、B、C、D、E目录中的文件数量一致
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2. **预处理需求**: 根据具体应用需求,必须进一步统一分辨率
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3. **去重**:虽然每组图像是人工单独标注的,但是为了避免重叠区域导致的验证集和训练集混淆,可根据坐标自行去重
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## 引用信息
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如果您在研究中使用了这个数据集,请引用:
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```bibtex
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@dataset{remote_sensing_change_detection_2025,
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title={remote-sensing-change-detection},
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author={Tingxuan Yan},
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year={2025},
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publisher={Hugging Face},
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howpublished={\url{https://huggingface.co/datasets/Mercyiris/remote-sensing-change-detection}}
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}
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```
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## 许可证
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本数据集采用 CC BY 4.0 许可证发布,允许自由使用、修改和分发,但需注明出处。
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## 联系方式
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如有任何问题或建议,请通过以下方式联系:
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- Hugging Face: [@Mercyiris](https://huggingface.co/Mercyiris)
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- 邮箱: [email protected]
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