Smart City Gnosys
Smart city article details
| Title | Lg-Umer: Unet-Like Network Integrate Local-Global Feature With Novel Attention For Road Extraction From Remote Sensing Images |
|---|---|
| ID_Doc | 35155 |
| Authors | Niu P.; Cai T.; Zhang Y.; Zhang P.; Xu W.; Gu J.; Han J. |
| Year | 2025 |
| Published | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| DOI | http://dx.doi.org/10.1109/JSTARS.2025.3573735 |
| Abstract | Road extraction from remote sensing images (RSIs) is a key research area in smart city development. While deep learning techniques have demonstrated remarkable effectiveness in this domain, existing approaches exhibit limitations: convolutional neural network (CNN)-based methods struggle to capture global contextual information for long-range road networks, vision transformer (ViT)-based methods fail to adequately extract multi-scale local features, and hybrid CNN-ViT architectures overlook the synergistic guidance between local and global features. To address these challenges, we propose LG-Umer, a UNet-like network that integrates Local-Global features with a novel attention mechanism, combining the complementary strengths of CNNs and ViTs within an encoder-decoder framework. Specifically, the encoder employs a Multi-scale Strip Deformational (MSD) module, which utilizes deformable convolutions to adaptively extract topological structures and variable-shaped local road features. In the decoder, a Multi-stage Gate Unit (MGU) module is introduced, incorporating a novel attention mechanism to model long-range dependencies by leveraging local features as attention operators for global feature refinement. Extensive experiments on three public benchmarks demonstrate the superiority of LG-Umer. It achieves IoU scores of 70.4%, 71.2% and 68.7% on the Massachusetts Road, DeepGlobe Road, and CHN6-CUG datasets, respectively, surpassing recent state-ofthe-art (SOTA) methods by 1.2%, 0.9%, and 1.1%. These results validate the effectiveness of our approach in balancing local detail preservation and global contextual modeling for road extraction tasks. © 2008-2012 IEEE. |
| Author Keywords | building extraction; deep learning; global attention; Multi-scale direction context-aware |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
46779  | 0.913 | Kumar K.M. | Roadtransnet: Advancing Remote Sensing Road Extraction Through Multi-Scale Features And Contextual Information | Signal, Image and Video Processing, 18, 3 (2024) |
| 789 | 0.896 | Zhou H.; He H.; Xu L.; Ma L.; Zhang D.; Chen N.; Chapman M.A.; Li J. | A Comparative Study Of Deep Learning Methods For Automated Road Network Extraction From High-Spatial-Resolution Remotely Sensed Imagery | Photogrammetric Engineering and Remote Sensing, 91, 3 (2025) |
| 43783 | 0.895 | Xiong Y.; Li L.; Wang H.; Ma T.; Wang Z.; Yang Y.; Wang S.; Zhang S. | Pvt-Unet: Road Extraction In Remote Sensing Imagery Based On U-Shaped Pyramid Vision Transformer Neural Network | ACM International Conference Proceeding Series (2024) |
| 48027 | 0.893 | Tao J.; Chen Z.; Sun Z.; Guo H.; Leng B.; Yu Z.; Wang Y.; He Z.; Lei X.; Yang J. | Seg-Road: A Segmentation Network For Road Extraction Based On Transformer And Cnn With Connectivity Structures | Remote Sensing, 15, 6 (2023) |
| 45994 | 0.891 | Xu Y.; Shi Z.; Xie X.; Chen Z.; Xie Z. | Residual Channel Attention Fusion Network For Road Extraction Based On Remote Sensing Images And Gps Trajectories | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17 (2024) |
| 17968 | 0.89 | Lu X.; Weng Q. | Deep Learning-Based Road Extraction From Remote Sensing Imagery: Progress, Problems, And Perspectives | ISPRS Journal of Photogrammetry and Remote Sensing, 228 (2025) |
| 60739 | 0.884 | Jiang Y.; Zhao J.; Luo W.; Guo B.; An Z.; Xu Y. | Utilizing Gcn-Based Deep Learning For Road Extraction From Remote Sensing Images | Sensors, 25, 13 (2025) |
| 48039 | 0.882 | Yu Z.; Chen Z.; Xiao K.; Lei X.; Tang R.; He Q.; Sun Z.; Guo H. | Segroadv2: A Hybrid Deformable Self-Attention And Convolutional Network For Road Extraction With Connectivity Structure | International Journal of Digital Earth, 18, 1 (2025) |
| 37204 | 0.878 | Quan K.; Yan D.; Feng R. | Mlff-Sfnet: A Visual Perception-Guided Network For High-Accuracy Road Extraction In Remote Sensing Imagery Via Multi-Level Feature Fusion And Similarity Filtering | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2025) |
| 19279 | 0.872 | Aslan E.; Özüpak Y. | Detection Of Road Extraction From Satellite Images With Deep Learning Method | Cluster Computing, 28, 1 (2025) |