Add image-segmentation pipeline tag, PyTorch library, and usage examples

This PR significantly enhances the model card for UAGLNet by:

- **Adding `pipeline_tag: image-segmentation`**: This ensures the model is discoverable under the relevant pipeline for building extraction tasks.
- **Adding `library_name: pytorch`**: Based on the installation instructions in the GitHub repository, the model uses PyTorch, enabling automated code snippets for users.
- **Enriching the content**:
- Removing the internal "File information" which is not meant for the public model card.
- Including a concise summary of the paper from the GitHub README.
- Adding the model's architecture image.
- Providing comprehensive "Quick Start" sections for installation and reproducing results (sample usage) directly from the GitHub README.
- Adding the citation information and acknowledgements.
- Linking to the full Apache 2.0 license file on GitHub.

These changes make the model card more informative and user-friendly, helping researchers and developers understand and utilize UAGLNet more effectively.

README.md

@@ -1,5 +1,7 @@
 ---
 license: apache-2.0
+pipeline_tag: image-segmentation
+library_name: pytorch
 ---
 
 # UAGLNet
@@ -9,3 +11,79 @@ license: apache-2.0
 **Authors:** [Dstate](https://github.com/Dstate) | **License:** Apache 2.0
 
 **Paper:** *“UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction”* ([arXiv:2512.12941](https://arxiv.org/abs/2512.12941))
+
+🔥 **UAGLNet has been accepted by IEEE TGRS**
+
+We present UAGLNet, which is capable to exploit high-quality global-local visual semantics under the guidance of uncertainty modeling. Specifically, we propose a novel cooperative encoder, which adopts hybrid CNN and transformer layers at different stages to capture the local and global visual semantics, respectively. An intermediate cooperative interaction block (CIB) is designed to narrow the gap between the local and global features when the network becomes deeper. Afterwards, we propose a Global-Local Fusion (GLF) module to complementarily fuse the global and local representations. Moreover, to mitigate the segmentation ambiguity in uncertain regions, we propose an Uncertainty-Aggregated Decoder (UAD) to explicitly estimate the pixel-wise uncertainty to enhance the segmentation accuracy. Extensive experiments demonstrate that our method achieves superior performance to other state-of-the-art methods.
+
+<img width="1000" src="https://github.com/Dstate/UAGLNet/raw/main/assets/architecture2.png">
+
+## Quick Start
+
+### Installation
+
+Clone this repository and create the environment.
+```bash
+git git@github.com:Dstate/UAGLNet.git
+cd UAGLNet
+
+conda create -n uaglnet python=3.8 -y
+conda activate uaglnet
+conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
+pip install -r requirements.txt
+```
+
+### Data Preprocessing
+
+We conduct experiments on the Inria, WHU, and Massachusetts datasets. Detailed guidance for dataset preprocessing is provided here: [DATA_PREPARATION.md](https://github.com/Dstate/UAGLNet/blob/main/assets/DATA_PREPARATION.md).
+
+### Training & Testing
+
+Training and testing examples on the Inria dataset:
+```bash
+# training
+python UAGLNet_train.py -c config/inria/UAGLNet.py
+
+# testing
+python UAGLNet_test.py -c config/inria/UAGLNet.py
+```
+
+### Main Results
+
+The following table presents the performance of UAGLNet on building extraction benchmarks.
+
+| **Benchmark** | **IoU** | **F1** | **P** | **R** | **Weight** |
+| :-------: | :--------: | :--------: | :-----------: | :------: | :------: |
+| Inria | 83.74 | 91.15 | 92.09 | 90.22 | [UAGLNet_Inria](https://huggingface.co/ldxxx/UAGLNet_Inria) |
+| Mass | 76.97 | 86.99 | 88.28 | 85.73 | [UAGLNet_Mass](https://huggingface.co/ldxxx/UAGLNet_Massachusetts) |
+| WHU | 92.07 | 95.87 | 96.21 | 95.54 | [UAGLNet_WHU](https://huggingface.co/ldxxx/UAGLNet_WHU) |
+
+You can quickly reproduce these results by running `Reproduce.py`, which will load the pretrained checkpoints from Hugging Face and perform inference.
+
+```bash
+# Inria
+python Reproduce.py -d Inria
+
+# Massachusetts
+python Reproduce.py -d Mass
+
+# WHU
+python Reproduce.py -d WHU
+```
+
+## Citation
+If you find this project useful in your research, please cite it as:
+```
+@article{UAGLNet,
+  title   = {UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction}, 
+  author  = {Siyuan Yao and Dongxiu Liu and Taotao Li and Shengjie Li and Wenqi Ren and Xiaochun Cao},
+  journal = {arXiv preprint arXiv:2512.12941},
+  year    = {2025}
+}
+```
+
+## Acknowledgement
+This work is built upon [BuildingExtraction](https://github.com/stdcoutzrh/BuildingExtraction), [GeoSeg](https://github.com/WangLibo1995/GeoSeg/tree/main) and [SMT](https://github.com/AFeng-x/SMT). We sincerely appreciate their contributions which provide a clear pipeline and well-organized code.
+
+## License
+This project is licensed under the [Apache License 2.0](https://github.com/Dstate/UAGLNet/blob/main/LICENSE).