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.
The following table presents the performance of UAGLNet on building extraction benchmarks.
1@article{UAGLNet,
2 title = {UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction},
3 author = {Siyuan Yao and Dongxiu Liu and Taotao Li and Shengjie Li and Wenqi Ren and Xiaochun Cao},
4 journal = {arXiv preprint arXiv:2512.12941},
5 year = {2025}
6}
