Update README.md

README.md

@@ -1,13 +1,44 @@
 ---
 license: apache-2.0
 pipeline_tag: image-segmentation
-library_name: pytorch
+tags:
+  - building-extraction
+  - remote-sensing
 ---
 
-# UAGLNet
+# UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction
 
-UAGLNet (Uncertainty-Aggregated Global-Local Fusion Network) is a model designed for building extraction from remote sensing images. It exploits high-quality global-local visual semantics under the guidance of uncertainty modeling, using a cooperative encoder with hybrid CNN and transformer layers.
+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"*.
 
-**Repository:** [https://github.com/Dstate/UAGLNet](https://github.com/Dstate/UAGLNet)
+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">
+
+## 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) |
+
+## 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}
+}
+```