---
license: apache-2.0
pipeline_tag: image-segmentation
library_name: pytorch
---

# UAGLNet

UAGLNet is an Uncertainty-Aggregated Global-Local Fusion Network designed for building extraction from remote sensing images. It exploits high-quality global-local visual semantics under the guidance of uncertainty modeling, addressing challenges posed by complex structural variations. The network features a novel cooperative encoder (hybrid CNN and transformer layers), an intermediate cooperative interaction block (CIB), a Global-Local Fusion (GLF) module, and an Uncertainty-Aggregated Decoder (UAD) to enhance segmentation accuracy by explicitly estimating pixel-wise uncertainty.

📄 **Paper:** "[UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction](https://huggingface.co/papers/2512.12941)" ([arXiv:2512.12941](https://arxiv.org/abs/2512.12941))
💻 **Repository:** [https://github.com/Dstate/UAGLNet](https://github.com/Dstate/UAGLNet)

## Sample Usage

You can quickly reproduce the main results for various datasets by running `Reproduce.py`, which will load the pretrained checkpoints from Hugging Face and perform inference.

```bash
# To reproduce results on the Inria dataset:
python Reproduce.py -d Inria

# To reproduce results on the Massachusetts dataset:
python Reproduce.py -d Mass

# To reproduce results on the WHU dataset:
python Reproduce.py -d WHU
```