ldxxx/UAGLNet_Backbone

UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction

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".

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

• ArXiv: 2512.12941
• Hugging Face Papers: 2512.12941

Code

• GitHub Repository: Dstate/UAGLNet
• Hugging Face Collection: ldxxx/uaglnet

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
Mass	76.97	86.99	88.28	85.73	UAGLNet_Mass
WHU	92.07	95.87	96.21	95.54	UAGLNet_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}
}