Improve model card: Add pipeline tag, usage, results, and detailed description

README.md

@@ -1,11 +1,90 @@
 ---
 license: apache-2.0
+pipeline_tag: image-segmentation
+tags:
+  - building-extraction
+  - remote-sensing
 ---
 
-# UAGLNet
+# UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction
 
-**Repository:** [Dstate/UAGLNet](https://github.com/Dstate/UAGLNet)
+This repository contains the official implementation of **UAGLNet**, a model for building extraction from remote sensing images, as presented in the paper *"UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction"*.
 
-**Authors:** [Dstate](https://github.com/Dstate) | **License:** Apache 2.0
+UAGLNet addresses the challenges of building extraction from remote sensing images due to complex structure variations. It proposes an Uncertainty-Aggregated Global-Local Fusion Network capable of exploiting high-quality global-local visual semantics under the guidance of uncertainty modeling. Specifically, it features a novel cooperative encoder with hybrid CNN and transformer layers, an intermediate cooperative interaction block (CIB) to narrow feature gaps, and a Global-Local Fusion (GLF) module. Additionally, an Uncertainty-Aggregated Decoder (UAD) is introduced to explicitly estimate pixel-wise uncertainty and mitigate segmentation ambiguity in uncertain regions.
 
-**Paper:** *“UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction”* ([arXiv:2512.12941](https://arxiv.org/abs/2512.12941))
+## Paper
+*   **ArXiv:** [2512.12941](https://arxiv.org/abs/2512.12941)
+*   **Hugging Face Papers:** [2512.12941](https://huggingface.co/papers/2512.12941)
+
+## Code
+*   **GitHub Repository:** [Dstate/UAGLNet](https://github.com/Dstate/UAGLNet)
+*   **Hugging Face Collection:** [ldxxx/uaglnet](https://huggingface.co/collections/ldxxx/uaglnet)
+
+<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 clone https://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:
+```bibtex
+@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.