SegEarth-R2: Towards Comprehensive Language-guided Segmentation for Remote Sensing Images

• 01/22/2026：训练集 和 验证集已发布。为方便参赛者更快搭建开发环境，现额外提供Conda环境包。
• 01/21/2026：Baseline代码已发布。
• 01/18/2026：赛题已发布，欢迎大家参赛！
• 12/24/2025：LaSeRS数据集将会作为 AIRS2026竞赛 中的一部分，因此数据延迟发布。欢迎大家关注！重要信息将会在此界面更新。
• 12/24/2025：SegEarth-R2 论文已发布。

🔧 Usage：

Follow the guidelines below to set up, train and evaluate:

• Preparation ⚙️: Instructions for organizing datasets and pretrained weights for proper model training and inference.
• Installation 💻: Set up the segearthr2 conda environment, install dependencies, and clone the repo.
• Training 🏋️‍♂️: Run scripts/train.sh with DeepSpeed, modifying parameters like data and model paths for training.
• Evaluation 🎯: Run scripts/eval.sh to evaluate the model, updating paths as needed.

⭐️ Citation

If you find this project useful, welcome to cite us.

@article{xin2025segearth,
  title={SegEarth-R2: Towards Comprehensive Language-guided Segmentation for Remote Sensing Images},
  author={Xin, Zepeng and Li, Kaiyu and Chen, Luodi and Li, Wanchen and Xiao, Yuchen and Qiao, Hui and Zhang, Weizhan and Meng, Deyu and Cao, Xiangyong},
  journal={arXiv preprint arXiv:2512.20013},
  year={2025}
}