Update README.md

README.md

@@ -1,54 +1,28 @@
 ---
 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
 
-**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)
 
-🔥 **UAGLNet has been accepted by IEEE TGRS**
+## Code
+*   **GitHub Repository:** [Dstate/UAGLNet](https://github.com/Dstate/UAGLNet)
+*   **Hugging Face Collection:** [ldxxx/uaglnet](https://huggingface.co/collections/ldxxx/uaglnet)
 
-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">
 
-<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
+## Main Results
 
 The following table presents the performance of UAGLNet on building extraction benchmarks.
 
@@ -58,32 +32,13 @@ The following table presents the performance of UAGLNet on building extraction b
 | 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.
-
-## License
-This project is licensed under the [Apache License 2.0](https://github.com/Dstate/UAGLNet/blob/main/LICENSE).
+```