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Add link to paper and github repo (#1)

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- Add link to paper and github repo (8f31c7a01621a8e17ab9f66104ccd7e397100dfd)


Co-authored-by: Niels Rogge <[email protected]>

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  1. README.md +14 -34
README.md CHANGED
@@ -20,10 +20,16 @@ dataset_info:
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  sequence:
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  - name: image
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  dtype: image
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- shape: [512, 512, 3]
 
 
 
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  - name: change_label
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  dtype: image
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- shape: [512, 512, 1]
 
 
 
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  splits:
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  - name: train
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  num_bytes: 1024000000
@@ -65,10 +71,15 @@ The JL1-CD dataset can be used to train and evaluate remote sensing image change
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  The **Multi-Teacher Knowledge Distillation (MTKD)** framework proposed in the paper achieves new state-of-the-art (SOTA) results on the JL1-CD dataset. Experiments demonstrate that the MTKD framework significantly improves the performance of change detection models with various network architectures and parameter sizes.
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  ### Citation
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  If you use the JL1-CD dataset, please cite the following paper:
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  ```bibtex
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  @article{liu2025jl1,
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  title={JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework},
@@ -106,35 +117,4 @@ For any questions or suggestions, please contact:
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  ### 数据集特点
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  - **高分辨率**:提供丰富的空间信息,便于视觉解释。
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- - **综合性**:涵盖多种变化类型,提升算法的泛化能力。
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- - **开源**:数据集完全开源,支持研究社区的使用和改进。
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-
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- ### 使用方式
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-
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- JL1-CD 数据集可用于训练和评估遥感图像变化检测模型。数据集中的图像对包含两个时间点的图像以及对应的像素级变化标签。用户可以使用该数据集来开发、测试和优化变化检测算法。
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- ### 基准结果
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- 论文中提出的 **多教师知识蒸馏框架(MTKD)** 在 JL1-CD 数据集上取得了最新的 state-of-the-art (SOTA) 结果。实验表明,MTKD 框架显著提升了多种网络架构和参数规模的变化检测模型的性能。
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-
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- ### 引用
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-
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- 如果您使用 JL1-CD 数据集,请引用以下论文:
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-
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- ```bibtex
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- @article{liu2025jl1,
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- title={JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework},
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- author={Liu, Ziyuan and Zhu, Ruifei and Gao, Long and Zhou, Yuanxiu and Ma, Jingyu and Gu, Yuantao},
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- journal={arXiv preprint arXiv:2502.13407},
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- year={2025}
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- }
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- ```
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-
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- ### 许可证
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- JL1-CD 数据集采用 **MIT License**,允许用户自由使用、修改和分发数据集。
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- ### 联系方式
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- 如有任何问题或建议,请联系:
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- - **刘子源**: [email protected]
 
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  sequence:
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  - name: image
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  dtype: image
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+ shape:
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+ - 512
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+ - 512
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+ - 3
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  - name: change_label
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  dtype: image
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+ shape:
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+ - 512
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+ - 512
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+ - 1
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  splits:
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  - name: train
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  num_bytes: 1024000000
 
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  The **Multi-Teacher Knowledge Distillation (MTKD)** framework proposed in the paper achieves new state-of-the-art (SOTA) results on the JL1-CD dataset. Experiments demonstrate that the MTKD framework significantly improves the performance of change detection models with various network architectures and parameter sizes.
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+ ### Code
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+ The code can be found at https://github.com/circleLZY/MTKD-CD.
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+
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  ### Citation
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  If you use the JL1-CD dataset, please cite the following paper:
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+ [JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework](https://huggingface.co/papers/2502.13407)
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+
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  ```bibtex
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  @article{liu2025jl1,
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  title={JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework},
 
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  ### 数据集特点
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  - **高分辨率**:提供丰富的空间信息,便于视觉解释。
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+ - **综合性**:涵盖多种变化类型,提升算法的泛化能力