MANAGING THE ROAD NETWORK OF URBAN AGGLOMERATION: FORMATION OF THE INFORMATION MODELING by T. V. Bobrova, A. A. Andryushenkov

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GSBYOLO: A lightweight Multi-Scale fusion network for road crack detection in complex environments by Yuhao Wang, Heran Zhu, Yirong Wang, Jianping Liu, Jun Xie, Bi Zhao, Siyue Zhao

“…Abstract Timely detection and regular maintenance of road cracks are critical for road and traffic safety. …”
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Socioeconomic Attributes in the Topology of the Intercity Road Network in Greece by Dimitrios Tsiotas

“…This paper studies the Greek interregional road network (GRN) using network, statistical, and empirical analysis. …”
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Analysis of Urban Road Network Robustness under Different Attack Conditions by Shuang tan, Lu Zhang

“…Then, the flow data of Origin-Destination (OD) pairs for the road network under normal operation is obtained using OD estimation module in TransCAD, the urban transportation network is modeled based on the actual traffic flow distribution. …”
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Visible Light Communication System Integrating Road Signs with the Vehicle Network Grid by Sameer Sami Hassan Al-Obaidi Sciences, Wisam Abed Shukur Sciences, Mohammed S. H. Al-Tamimi Sciences, Bogdan Ghita Sciences

“…In addition, all this information collected from different vehicles is processed in the Network Road Data Centre. The result will be a signal of attention and order to the Driver and ECU of the vehicle to reduce the speed and stop the vehicle. …”
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Enhancing Road Drainage Systems for Extreme Storms: Integration of a High-Precision Flow Diversion Module into SWMM Code by Yuting Ren, Zhiyu Shao, Qi Zhang, Wang Feng, Lei Xu, Huafeng Gong, Scott Yost, Lei Chen, Hongxiang Chai

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Semantic Segmentation Network for Unstructured Rural Roads Based on Improved SPPM and Fused Multiscale Features by Xinyu Cao, Yongqiang Tian, Zhixin Yao, Yunjie Zhao, Taihong Zhang

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kLCRNet: Fast Road Network Extraction via Keypoint-Driven Local Connectivity Exploration by Mingming Zhang, Bin Wang, Shuai Yang, Qingjie Liu, Yunhong Wang

“…In this article, we present kLCRNet, an efficient road network extraction framework that overcomes these limitations by leveraging keypoint-driven local connectivity exploration. kLCRNet consists of two key components: A keypoint detection module that identifies road keypoints via heatmap-based detection and refines them using bipartite matching, and a local connectivity exploration module that samples local connection relationships to directly construct connectivity between detected keypoints. …”
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DAPONet: A Dual Attention and Partially Overparameterized Network for Real-Time Road Damage Detection by Weichao Pan, Jianmei Lei, Xu Wang, Chengze Lv, Gongrui Wang, Chong Li

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Transferable Contextual Network for Rural Road Extraction from UAV-Based Remote Sensing Images by Jian Wang, Renlong Wang, Yahui Liu, Fei Zhang, Ting Cheng

“…To address these challenges, we propose a transferable contextual network (TCNet), designed to enhance the transferability and accuracy of rural road extraction. …”
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Multi-feature fusion and multi-attention deep network for enhancing road extraction in remote sensing images by Haitao Xu, Lin Zhou, Bo Huang, Shiwan Chen

“…Multi-level dual residual blocks are introduced to capture multi-scale and multi-level road features. A channel attention feature fusion module fuses road features from the decoder while suppressing coarse-grain noises. …”
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ANF-Net: A Refined Segmentation Network for Road Scenes with Multiple Noises and Various Morphologies of Cracks by Xiao Hu, Qihao Chen, Xiuguo Liu, Gang Deng, Cheng Chi, Bin Wang

“…When extracting crack features, on one hand, the network introduces an attention module tailored for crack scenes to learn pixel-wise feature weights, enabling the network to focus on crack regions and thereby reducing the impact of similar background features, mitigating false positives caused by noise misclassification. …”
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Swin-GAT Fusion Dual-Stream Hybrid Network for High-Resolution Remote Sensing Road Extraction by Hongkai Zhang, Hongxuan Yuan, Minghao Shao, Junxin Wang, Suhong Liu

“…Overall, this dual-stream dynamic-fusion network sets a new benchmark for remote sensing road extraction and holds promise for real-world, real-time applications.…”
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BD-WNet: Boundary Decoupling-Based W-Shape Network for Road Segmentation in Optical Remote Sensing Imagery by Shilong Fan, Jianlin Xie, Yin Zhuang, He Chen, Zhibao Su, Lianlin Li

“…First, a novel W-shaped double encoder–decoder architecture network is designed to provide more stable semantic description, which can be used for road body extraction. …”
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SegRoadv2: a hybrid deformable self-attention and convolutional network for road extraction with connectivity structure by Zhengbo Yu, Zhe Chen, Keyan Xiao, Xiangqi Lei, Rui Tang, Qiaoran He, Zhongchang Sun, Huadong Guo

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Enhancing Road Scene Segmentation With an Optimized DeepLabV3+ by Zhe Ren, Libao Wang, Tianming Song, Yihang Li, Jian Zhang, Fengfeng Zhao

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Predicting the Behavior of Road Users in Rural Areas for Self-Driving Cars by S. A. Ivanov, B. Rasheed

“…Introduction. The prediction module generates possible future trajectories of dynamic objects that enables a self-driving vehicle to move safely on public roads. …”
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Swin-FSNet: A Frequency-Aware and Spatially Enhanced Network for Unpaved Road Extraction from UAV Remote Sensing Imagery by Jiwu Guan, Qingzhan Zhao, Wenzhong Tian, Xinxin Yao, Jingyang Li, Wei Li

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Robust scale fusion and edge-aware feature attention network for remote sensing UAV road detection under harsh weather by Jialang Liu, Jialei Zhan, Jiehua Zhang, Jiangming Chen, Yan Song, Lixing Tang, Le Zhou, Chengsi Du, Yingmei Wei, Yanming Guo

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Cross-Domain Feature Fusion Network: A Lightweight Road Extraction Model Based on Multi-Scale Spatial-Frequency Feature Fusion by Lin Gao, Tianyang Shi, Lincong Zhang

“…To fully extract and effectively fuse spatial and frequency domain features, we propose a Cross-Domain Feature Fusion Network (CDFFNet). The framework consists of three main components: the Atrous Bottleneck Pyramid Module (ABPM), the Frequency Band Feature Separator (FBFS), and the Domain Fusion Module(DFM). …”
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