Smart City Gnosys
Smart city article details
| Title | Utilizing Gcn-Based Deep Learning For Road Extraction From Remote Sensing Images |
|---|---|
| ID_Doc | 60739 |
| Authors | Jiang Y.; Zhao J.; Luo W.; Guo B.; An Z.; Xu Y. |
| Year | 2025 |
| Published | Sensors, 25, 13 |
| DOI | http://dx.doi.org/10.3390/s25133915 |
| Abstract | The technology of road extraction serves as a crucial foundation for urban intelligent renewal and green sustainable development. Its outcomes can optimize transportation network planning, reduce resource waste, and enhance urban resilience. Deep learning-based approaches have demonstrated outstanding performance in road extraction, particularly excelling in complex scenarios. However, extracting roads from remote sensing data remains challenging due to several factors that limit accuracy: (1) Roads often share similar visual features with the background, such as rooftops and parking lots, leading to ambiguous inter-class distinctions; (2) Roads in complex environments, such as those occluded by shadows or trees, are difficult to detect. To address these issues, this paper proposes an improved model based on Graph Convolutional Networks (GCNs), named FR-SGCN (Hierarchical Depth-wise Separable Graph Convolutional Network Incorporating Graph Reasoning and Attention Mechanisms). The model is designed to enhance the precision and robustness of road extraction through intelligent techniques, thereby supporting precise planning of green infrastructure. First, high-dimensional features are extracted using ResNeXt, whose grouped convolution structure balances parameter efficiency and feature representation capability, significantly enhancing the expressiveness of the data. These high-dimensional features are then segmented, and enhanced channel and spatial features are obtained via attention mechanisms, effectively mitigating background interference and intra-class ambiguity. Subsequently, a hybrid adjacency matrix construction method is proposed, based on gradient operators and graph reasoning. This method integrates similarity and gradient information and employs graph convolution to capture the global contextual relationships among features. To validate the effectiveness of FR-SGCN, we conducted comparative experiments using 12 different methods on both a self-built dataset and a public dataset. The proposed model achieved the highest F1 score on both datasets. Visualization results from the experiments demonstrate that the model effectively extracts occluded roads and reduces the risk of redundant construction caused by data errors during urban renewal. This provides reliable technical support for smart cities and sustainable development. © 2025 by the authors. |
| Author Keywords | depthwise separable convolution; gradient operator; graph convolution; graph reasoning; road extraction; smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
17968  | 0.929 | 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) |
| 46779 | 0.919 | 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 | 0.912 | 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) |
| 45994 | 0.906 | 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) |
| 37204 | 0.904 | 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) |
| 4194 | 0.898 | Pruthi J.; Dhingra S. | A Review Of Research On Road Feature Extraction Through Remote Sensing Images Based On Deep Learning Algorithms | Proceedings - 2023 3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023 (2023) |
| 19279 | 0.89 | Aslan E.; Özüpak Y. | Detection Of Road Extraction From Satellite Images With Deep Learning Method | Cluster Computing, 28, 1 (2025) |
| 19850 | 0.887 | Zhang C.; Zhang H.; Guo X.; Qi H.; Zhao Z.; Tang L. | Df-Bsfnet: A Bilateral Synergistic Fusion Network With Novel Dynamic Flow Convolution For Robust Road Extraction | Information Fusion, 118 (2025) |
| 48027 | 0.886 | 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) |
| 28317 | 0.885 | Lu X.; Zhong Y.; Zheng Z.; Chen D. | Gre And Beyond: A Global Road Extraction Dataset | International Geoscience and Remote Sensing Symposium (IGARSS), 2022-July (2022) |