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Search Results (747)

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Keywords = road network extraction

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27 pages, 5776 KB  
Article
R-SWTNet: A Context-Aware U-Net-Based Framework for Segmenting Rural Roads and Alleys in China with the SQVillages Dataset
by Jianing Wu, Junqi Yang, Xiaoyu Xu, Ying Zeng, Yan Cheng, Xiaodong Liu and Hong Zhang
Land 2025, 14(10), 1930; https://doi.org/10.3390/land14101930 - 23 Sep 2025
Viewed by 101
Abstract
Rural road networks are vital for rural development, yet narrow alleys and occluded segments remain underrepresented in digital maps due to irregular morphology, spectral ambiguity, and limited model generalization. Traditional segmentation models struggle to balance local detail preservation and long-range dependency modeling, prioritizing [...] Read more.
Rural road networks are vital for rural development, yet narrow alleys and occluded segments remain underrepresented in digital maps due to irregular morphology, spectral ambiguity, and limited model generalization. Traditional segmentation models struggle to balance local detail preservation and long-range dependency modeling, prioritizing either local features or global context alone. Hypothesizing that integrating hierarchical local features and global context will mitigate these limitations, this study aims to accurately segment such rural roads by proposing R-SWTNet, a context-aware U-Net-based framework, and constructing the SQVillages dataset. R-SWTNet integrates ResNet34 for hierarchical feature extraction, Swin Transformer for long-range dependency modeling, ASPP for multi-scale context fusion, and CAM-Residual blocks for channel-wise attention. The SQVillages dataset, built from multi-source remote sensing imagery, includes 18 diverse villages with adaptive augmentation to mitigate class imbalance. Experimental results show R-SWTNet achieves a validation IoU of 54.88% and F1-score of 70.87%, outperforming U-Net and Swin-UNet, and with less overfitting than R-Net and D-LinkNet. Its lightweight variant supports edge deployment, enabling on-site road management. This work provides a data-driven tool for infrastructure planning under China’s Rural Revitalization Strategy, with potential scalability to global unstructured rural road scenes. Full article
(This article belongs to the Section Land Innovations – Data and Machine Learning)
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29 pages, 2906 KB  
Article
Spatiotemporal Graph Convolutional Network-Based Long Short-Term Memory Model with A* Search Path Navigation and Explainable Artificial Intelligence for Carbon Monoxide Prediction in Northern Cape Province, South Africa
by Israel Edem Agbehadji and Ibidun Christiana Obagbuwa
Atmosphere 2025, 16(9), 1107; https://doi.org/10.3390/atmos16091107 - 21 Sep 2025
Viewed by 253
Abstract
Background: The emission of air pollutants into the atmosphere is a global issue as it contributes to global warming and climate-related issues. Human activities like the burning of fossil fuel influence changes in weather patterns—resulting in issues such as a rise in sea [...] Read more.
Background: The emission of air pollutants into the atmosphere is a global issue as it contributes to global warming and climate-related issues. Human activities like the burning of fossil fuel influence changes in weather patterns—resulting in issues such as a rise in sea levels, among other things. Identifying road network routes within Northern Cape Province in South Africa that are less exposed to air pollutants like carbon monoxide is the issue this study seeks to address. Methods: The method used for our predictions is based on a graph convolutional network (GCN) and long short-term memory (LSTM). The GCN extracts geospatial characteristics, and the LSTM captures both nonlinear relationships and temporal dependencies in an air pollutant and meteorological dataset. Furthermore, an A* search strategy identifies the path from one location to another with the lowest carbon monoxide concentrations within a road network. The explainable artificial intelligence (xAI) technique is used to describe the nonlinear relationship between the target variable and features. Meteorological and air pollutant data in the form of statistical mean, minimum, and maximum values were leveraged, and a random sampling technique was utilized to fill the data gap to help train the predictive model (GCN-LSTM-A*). Results: The predictive model was evaluated with mean squared error (MSE) and root mean squared error (RMSE) values within two multi-time steps (8 and 16 h) with MSEs of 0.1648 and 0.1701, respectively. The LIME technique, which provides explanations of features, shows that Wind_speed and NO2 and NOx concentrations decreased the predicted CO, whereas PM2.5, PM10, relative humidity, and O3 increased the predicted CO of the route. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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20 pages, 39725 KB  
Article
TFP-YOLO: Obstacle and Traffic Sign Detection for Assisting Visually Impaired Pedestrians
by Zhiwei Zheng, Jin Cheng and Fanghua Jin
Sensors 2025, 25(18), 5879; https://doi.org/10.3390/s25185879 - 19 Sep 2025
Viewed by 299
Abstract
With the increasing demand for intelligent mobility assistance among the visually impaired, machine guide dogs based on computer vision have emerged as an effective alternative to traditional guide dogs, owing to their flexible deployment and scalability. To enhance their visual perception capabilities in [...] Read more.
With the increasing demand for intelligent mobility assistance among the visually impaired, machine guide dogs based on computer vision have emerged as an effective alternative to traditional guide dogs, owing to their flexible deployment and scalability. To enhance their visual perception capabilities in complex urban environments, this paper proposes an improved YOLOv8-based detection algorithm, termed TFP-YOLO, designed to recognize traffic signs such as traffic lights and crosswalks, as well as small obstacle objects including pedestrians and bicycles, thereby improving the target detection performance of machine guide dogs in complex road scenarios. The proposed algorithm incorporates a Triplet Attention mechanism into the backbone network to strengthen the perception of key regions, and integrates a Triple Feature Encoding (TFE) module to achieve collaborative extraction of both local and global features. Additionally, a P2 detection head is introduced to improve the accuracy of small object detection, particularly for traffic lights. Furthermore, the WIoU loss function is adopted to enhance training stability and the model’s generalization capability. Experimental results demonstrate that the proposed algorithm achieves a detection accuracy of 93.9% and a precision of 90.2%, while reducing the number of parameters by 17.2%. These improvements significantly enhance the perception performance of machine guide dogs in identifying traffic information and obstacles, providing strong technical support for subsequent path planning and embedded deployment, and demonstrating considerable practical application value. Full article
(This article belongs to the Section Intelligent Sensors)
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16 pages, 2981 KB  
Article
CNN-Based Road Event Detection Using Multiaxial Vibration and Acceleration Signals
by Abiel Aguilar-González and Alejandro Medina Santiago
Appl. Sci. 2025, 15(18), 10203; https://doi.org/10.3390/app151810203 - 19 Sep 2025
Viewed by 388
Abstract
Road event detection plays a key role in tasks such as monitoring, anomaly identification, and urban traffic optimization. Conventional methods often rely on feature extraction and classification or classical machine learning models, which may struggle when processing high-frequency signals in real time. In [...] Read more.
Road event detection plays a key role in tasks such as monitoring, anomaly identification, and urban traffic optimization. Conventional methods often rely on feature extraction and classification or classical machine learning models, which may struggle when processing high-frequency signals in real time. In this work, we propose a CNN-based classification approach designed to handle multi-axial acceleration and vibration signals captured from road scenarios. Instead of relying on static feature sets, our method leverages a convolutional neural network architecture capable of automatically learning discriminative patterns from raw sensor data. We structure the time-series input into temporal windows and use it to train models that can identify different event categories, including “Speed Bumps”, “Potholes”, and “Sudden Braking” events. The experimental results show that our approach achieves an accuracy of 93.51%, with a precision of 93.56% and a recall of 93.51%. Further, the average AUC score of 0.9855 confirms the strong discriminative power of our proposal. In contrast to rule-based methods, which require frequent tuning to adapt to new datasets, our approach generalizes better across different road conditions and offers a practical alternative for real-time deployment in dynamic environments, outperforming rule-based approaches by over 10% in F1-score, while preserving deployment efficiency on embedded hardware. Full article
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21 pages, 5459 KB  
Article
Research on Road Surface Recognition Algorithm Based on Vehicle Vibration Data
by Jianfeng Cui, Hengxu Zhang, Xiao Wang, Yu Jing and Xiujian Chou
Sensors 2025, 25(18), 5642; https://doi.org/10.3390/s25185642 - 10 Sep 2025
Viewed by 316
Abstract
Road surface conditions significantly impact driving safety and maintenance costs. Especially in connected and automated vehicles (CAVs), the road surface type recognition is critical for environmental perception. Traditional road surface recognition methods face limitations in feature extraction, so an improved one-dimensional convolutional neural [...] Read more.
Road surface conditions significantly impact driving safety and maintenance costs. Especially in connected and automated vehicles (CAVs), the road surface type recognition is critical for environmental perception. Traditional road surface recognition methods face limitations in feature extraction, so an improved one-dimensional convolutional neural network (1D-CNN) algorithm was proposed based on the VGG16 architecture. A vibration signal acquisition system was developed to efficiently acquire high-quality vehicle vibration signals. The optimized 1D-CNN algorithm model contains only 101.6 k parameters, significantly reducing computational cost and training time while maintaining high accuracy. Data augmentation, Adam optimization algorithm and L2 regularization were integrated to enhance generalization capabilities and suppress overfitting. On public datasets and actual vehicles tests, recognition accuracy rate reached 99.3% and 99.4%, respectively, substantially outperforming conventional methods. The algorithm also exhibited strong adaptability to different data sources. The research findings have implications for the accurate and efficient identification of road surfaces. Full article
(This article belongs to the Section Vehicular Sensing)
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21 pages, 5368 KB  
Article
Predicting Urban Traffic Under Extreme Weather by Deep Learning Method with Disaster Knowledge
by Jiting Tang, Yuyao Zhu, Saini Yang and Carlo Jaeger
Appl. Sci. 2025, 15(17), 9848; https://doi.org/10.3390/app15179848 - 8 Sep 2025
Viewed by 1226
Abstract
Meteorological and climatological trends are surely changing the way urban infrastructure systems need to be operated and maintained. Urban road traffic fluctuates more significantly under the interference of strong wind–rain weather, especially during tropical cyclones. Deep learning-based methods have significantly improved the accuracy [...] Read more.
Meteorological and climatological trends are surely changing the way urban infrastructure systems need to be operated and maintained. Urban road traffic fluctuates more significantly under the interference of strong wind–rain weather, especially during tropical cyclones. Deep learning-based methods have significantly improved the accuracy of traffic prediction under extreme weather, but their robustness still has much room for improvement. As the frequency of extreme weather events increases due to climate change, accurately predicting spatiotemporal patterns of urban road traffic is crucial for a resilient transportation system. The compounding effects of the hazards, environments, and urban road network determine the spatiotemporal distribution of urban road traffic during an extreme weather event. In this paper, a novel Knowledge-driven Attribute-Augmented Attention Spatiotemporal Graph Convolutional Network (KA3STGCN) framework is proposed to predict urban road traffic under compound hazards. We design a disaster-knowledge attribute-augmented unit to enhance the model’s ability to perceive real-time hazard intensity and road vulnerability. The attribute-augmented unit includes the dynamic hazard attributes and static environment attributes besides the road traffic information. In addition, we improve feature extraction by combining Graph Convolutional Network, Gated Recurrent Unit, and the attention mechanism. A real-world dataset in Shenzhen City, China, was employed to validate the proposed framework. The findings show that the prediction accuracy of traffic speed can be significantly increased by 12.16%~31.67% with disaster information supplemented, and the framework performs robustly on different road vulnerabilities and hazard intensities. The framework can be migrated to other regions and disaster scenarios in order to strengthen city resilience. Full article
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17 pages, 2128 KB  
Article
Vision-Based Highway Lane Extraction from UAV Imagery: A Deep Learning and Geometric Constraints Approach
by Jin Wang, Guangjun He, Xiuwang Dai, Feng Wang and Yanxin Zhang
Electronics 2025, 14(17), 3554; https://doi.org/10.3390/electronics14173554 - 6 Sep 2025
Viewed by 530
Abstract
The rapid evolution of unmanned aerial vehicle (UAV) technology and low-altitude economic development have propelled drone applications in critical infrastructure monitoring, particularly in intelligent transportation systems where real-time aerial image processing has emerged as a pressing requirement. We address the pivotal challenge of [...] Read more.
The rapid evolution of unmanned aerial vehicle (UAV) technology and low-altitude economic development have propelled drone applications in critical infrastructure monitoring, particularly in intelligent transportation systems where real-time aerial image processing has emerged as a pressing requirement. We address the pivotal challenge of highway lane extraction from low-altitude UAV perspectives by applying a novel three-stage framework. This framework consists of (1) a deep learning-based semantic segmentation module that employs an enhanced STDC network with boundary-aware loss for precise detection of roads and lane markings; (2) an optimized polynomial fitting algorithm incorporating iterative classification and adaptive error thresholds to achieve robust lane marking consolidation; and (3) a global optimization module designed for context-aware lane generation. Our methodology demonstrates superior performance with 94.11% F1-score and 93.84% IoU, effectively bridging the technical gap in UAV-based lane extraction while establishing a reliable foundation for advanced traffic monitoring applications. Full article
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20 pages, 2077 KB  
Article
OTVLD-Net: An Omni-Dimensional Dynamic Convolution-Transformer Network for Lane Detection
by Yunhao Wu, Ziyao Zhang, Haifeng Chen and Li Jian
Sensors 2025, 25(17), 5475; https://doi.org/10.3390/s25175475 - 3 Sep 2025
Viewed by 608
Abstract
With the vigorous development of deep learning technology, lane detection tasks have achieved phased results. However, existing lane detection models do not consider the unique geometric and visual features of lanes when dealing with some challenging scenarios, resulting in many difficulties and limitations. [...] Read more.
With the vigorous development of deep learning technology, lane detection tasks have achieved phased results. However, existing lane detection models do not consider the unique geometric and visual features of lanes when dealing with some challenging scenarios, resulting in many difficulties and limitations. To this end, we propose a lane detection network based on full-dimensional convolutional Transformer (OTVLD-Net) to improve the adaptability of the model under extreme road conditions and better handle complex lane topology. In order to extract richer contextual features, we designed ODVT-Net, which uses full-dimensional dynamic convolution combined with improved feature flip fusion layer and non-local network layer, and aggregates lane symmetry features by utilizing the horizontal symmetry of lanes. A feature weight generation mechanism based on Transformer is designed, and a cross-attention mechanism between feature maps and lane requests is added in the decoding stage to enable the network to aggregate global feature information. At the same time, a vanishing point detection module is introduced, and a joint weighted loss function is designed to be trained in coordination with the lane detection task to improve the generalization ability of the lane detection model. Experimental results on the OpenLane and CurveLanes datasets show that the detection effect of the OTVLD-Net model has reached the current advanced level. In particular, the accuracy on the OpenLane dataset is 6.4% higher than the F1 score of the second-ranked model, and the average performance in different challenging scenarios is also improved by 8.9%. At the same time, when ResNet-18 is used as the template feature extraction network, the model achieves a speed of 103FPS and a computing power of 14.2 GFlops, achieving good performance while ensuring real-time performance. Full article
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23 pages, 5998 KB  
Article
An Enhanced Feature Extraction and Multi-Branch Occlusion Discrimination Network for Road Detection from Satellite Imagery
by Ruixiang Wu, Lun Zhang, Longkai Guan, Xiangrong Ni and Jianxing Gong
Remote Sens. 2025, 17(17), 3037; https://doi.org/10.3390/rs17173037 - 1 Sep 2025
Viewed by 804
Abstract
Extracting road network information from satellite remote sensing images is an effective method of dealing with dynamic changes in road networks. At present, the use of deep learning methods to automatically segment road networks from remote sensing images has become mainstream. However, existing [...] Read more.
Extracting road network information from satellite remote sensing images is an effective method of dealing with dynamic changes in road networks. At present, the use of deep learning methods to automatically segment road networks from remote sensing images has become mainstream. However, existing methods often produce fragmented extraction results. This is usually caused by insufficient feature extraction and occlusion. In order to solve these problems, we propose an enhanced feature extraction and multi-branch occlusion discrimination network (EFMOD-Net) based on an encoder–decoder architecture. Firstly, a multi-directional feature extraction (MFE) module was proposed as the input for the network, which utilizes multi-directional strip convolution for feature extraction to better capture the linear features of the road. Subsequently, an enhanced feature extraction (EFE) module was designed to enhance the performance of the model in the feature extraction stage by using a dual-branch structure. The proposed multi-branch occlusion discrimination (MOD) module combines the attention mechanism and strip convolution to learn the topological relationship between pixels, enhance the network’s detection of occlusion and complex backgrounds, and reduce the generation of road debris. On the public dataset, the proposed method is compared with other SOTA methods. The experimental results show that the network designed in this paper achieves an IoU of 64.73 and 63.58 on the DeepGlobe and CHN6-CUG datasets, respectively, which is 1.66% and 1.84% higher than the IoU of performance-based methods. The proposed method combines multi-directional bar convolution and a multi-branch structure for road extraction, which provides a new idea for linear object segmentation in complex backgrounds that could be applied directly to urban renewal, disaster assessment, and other application scenarios. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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27 pages, 1057 KB  
Review
Distributed Acoustic Sensing for Road Traffic Monitoring: Principles, Signal Processing, and Emerging Applications
by Jingxiang Deng, Long Jin, Hongzhi Wang, Zihao Zhang, Yanjiang Liu, Fei Meng, Jikai Wang, Zhenghao Li and Jianqing Wu
Infrastructures 2025, 10(9), 228; https://doi.org/10.3390/infrastructures10090228 - 29 Aug 2025
Viewed by 900
Abstract
With accelerating urbanization and the exponential growth in vehicle populations, high-precision traffic monitoring has become indispensable for intelligent transportation systems (ITSs). Conventional sensing technologies—including inductive loops, cameras, and radar—suffer from inherent limitations: restrictive spatial coverage, prohibitive installation costs, and vulnerability to adverse weather. [...] Read more.
With accelerating urbanization and the exponential growth in vehicle populations, high-precision traffic monitoring has become indispensable for intelligent transportation systems (ITSs). Conventional sensing technologies—including inductive loops, cameras, and radar—suffer from inherent limitations: restrictive spatial coverage, prohibitive installation costs, and vulnerability to adverse weather. Distributed Acoustic Sensing (DAS), leveraging Rayleigh backscattering to convert standard optical fibers into kilometer-scale, real-time vibration sensor networks, presents a transformative alternative. This paper provides a comprehensive review of the principles and system architecture of DAS for roadway traffic monitoring, with a focus on signal processing techniques, feature extraction methods, and recent advances in vehicle detection, classification, and speed/flow estimation. Special attention is given to the integration of deep learning approaches, which enhance noise suppression and feature recognition under complex, multi-lane traffic conditions. Real-world deployment cases on highways, urban roads, and bridges are analyzed to demonstrate DAS’s practical value. Finally, this paper delineates emerging research trends and technical hurdles, providing actionable insights for the scalable implementation of DAS-enhanced ITS infrastructures. Full article
(This article belongs to the Special Issue Sustainable Road Design and Traffic Management)
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17 pages, 2183 KB  
Article
Data-Driven Pseudo-Crack Cognition and Removal for Intelligent Pavement Inspection with Gradient Priority and Self-Attention
by Renping Xie, Lin Liu, Mengyao Chen, Chenxi Pang and Ming Tao
Big Data Cogn. Comput. 2025, 9(9), 221; https://doi.org/10.3390/bdcc9090221 - 27 Aug 2025
Viewed by 457
Abstract
Road surface cracks are the most common and significant diseases in concrete pavement inspection. However, the presence of crack-like edges on objects such as water stains, fallen leaves, and ruts often result in the false detection of concrete pavement cracks. To better recognize [...] Read more.
Road surface cracks are the most common and significant diseases in concrete pavement inspection. However, the presence of crack-like edges on objects such as water stains, fallen leaves, and ruts often result in the false detection of concrete pavement cracks. To better recognize pseudo-cracks, we first construct a novel dataset containing real pseudo-crack images for training and evaluation. To distinguish pseudo-cracks within images, a gradient prior is introduced to enhance the network’s perception of the detailed changes in crack edges, thereby improving its crack localization capability. Next, a self-attention mechanism is employed to focus on the extraction of global crack features, effectively mitigating interference from pseudo-crack features. Subsequently, deep global semantic features are fused with shallow detail features through dense connections, enriching feature extraction while circumventing the issue of edge gradient disappearance often encountered in deeper networks. Finally, the concatenation of deep global features with shallow detail features enhances the utilization of effective features, enabling robust pseudo-crack removal and preserving the continuity and integrity of the detected cracks. To validate the effectiveness of the proposed approach, we conduct comparative experiments with several crack detection methods across multiple datasets. The results demonstrate that our method achieves superior performance in both quantitative indicators and visual effects. Full article
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16 pages, 306 KB  
Article
Adaptive Cross-Scale Graph Fusion with Spatio-Temporal Attention for Traffic Prediction
by Zihao Zhao, Xingzheng Zhu and Ziyun Ye
Electronics 2025, 14(17), 3399; https://doi.org/10.3390/electronics14173399 - 26 Aug 2025
Viewed by 469
Abstract
Traffic flow prediction is a critical component of intelligent transportation systems, playing a vital role in alleviating congestion, improving road resource utilization, and supporting traffic management decisions. Although deep learning methods have made remarkable progress in this field in recent years, current studies [...] Read more.
Traffic flow prediction is a critical component of intelligent transportation systems, playing a vital role in alleviating congestion, improving road resource utilization, and supporting traffic management decisions. Although deep learning methods have made remarkable progress in this field in recent years, current studies still face challenges in modeling complex spatio-temporal dependencies, adapting to anomalous events, and generalizing to large-scale real-world scenarios. To address these issues, this paper proposes a novel traffic flow prediction model. The proposed approach simultaneously leverages temporal and frequency domain information and introduces adaptive graph convolutional layers to replace traditional graph convolutions, enabling dynamic capture of traffic network structural features. Furthermore, we design a frequency–temporal multi-head attention mechanism for effective multi-scale spatio-temporal feature extraction and develop a cross-multi-scale graph fusion strategy to enhance predictive performance. Extensive experiments on real-world datasets, PeMS and Beijing, demonstrate that our method significantly outperforms state-of-the-art (SOTA) baselines. For example, on the PeMS20 dataset, our model achieves a 53.6% lower MAE, a 12.3% lower NRMSE, and a 3.2% lower MAPE than the best existing method (STFGNN). Moreover, the proposed model achieves competitive computational efficiency and inference speed, making it well-suited for practical deployment. Full article
(This article belongs to the Special Issue Graph-Based Learning Methods in Intelligent Transportation Systems)
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25 pages, 9065 KB  
Article
PWFNet: Pyramidal Wavelet–Frequency Attention Network for Road Extraction
by Jinkun Zong, Yonghua Sun, Ruozeng Wang, Dinglin Xu, Xue Yang and Xiaolin Zhao
Remote Sens. 2025, 17(16), 2895; https://doi.org/10.3390/rs17162895 - 20 Aug 2025
Viewed by 833
Abstract
Road extraction from remote sensing imagery plays a critical role in applications such as autonomous driving, urban planning, and infrastructure development. Although deep learning methods have achieved notable progress, current approaches still struggle with complex backgrounds, varying road widths, and strong texture interference, [...] Read more.
Road extraction from remote sensing imagery plays a critical role in applications such as autonomous driving, urban planning, and infrastructure development. Although deep learning methods have achieved notable progress, current approaches still struggle with complex backgrounds, varying road widths, and strong texture interference, often leading to fragmented road predictions or the misclassification of background regions. Given that roads typically exhibit smooth low-frequency characteristics while background clutter tends to manifest in mid- and high-frequency ranges, incorporating frequency-domain information can enhance the model’s structural perception and discrimination capabilities. To address these challenges, we propose a novel frequency-aware road extraction network, termed PWFNet, which combines frequency-domain modeling with multi-scale feature enhancement. PWFNet comprises two key modules. First, the Pyramidal Wavelet Convolution (PWC) module employs multi-scale wavelet decomposition fused with localized convolution to accurately capture road structures across various spatial resolutions. Second, the Frequency-aware Adjustment Module (FAM) partitions the Fourier spectrum into multiple frequency bands and incorporates a spatial attention mechanism to strengthen low-frequency road responses while suppressing mid- and high-frequency background noise. By integrating complementary modeling from both spatial and frequency domains, PWFNet significantly improves road continuity, edge clarity, and robustness under complex conditions. Experiments on the DeepGlobe and CHN6-CUG road datasets demonstrate that PWFNet achieves IoU improvements of 3.8% and 1.25% over the best-performing baseline methods, respectively. In addition, we conducted cross-region transfer experiments by directly applying the trained model to remote sensing images from different geographic regions and at varying resolutions to assess its generalization capability. The results demonstrate that PWFNet maintains the continuity of main and branch roads and preserves edge details in these transfer scenarios, effectively reducing false positives and missed detections. This further validates its practicality and robustness in diverse real-world environments. Full article
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31 pages, 4278 KB  
Article
Acoustic Analysis of Semi-Rigid Base Asphalt Pavements Based on Transformer Model and Parallel Cross-Gate Convolutional Neural Network
by Changfeng Hao, Min Ye, Boyan Li and Jiale Zhang
Appl. Sci. 2025, 15(16), 9125; https://doi.org/10.3390/app15169125 - 19 Aug 2025
Viewed by 340
Abstract
Semi-rigid base asphalt pavements, a common highway structure in China, often suffer from debonding defects which reduce road stability and shorten service life. In this study, a new method of road debonding detection based on the acoustic vibration method is proposed to address [...] Read more.
Semi-rigid base asphalt pavements, a common highway structure in China, often suffer from debonding defects which reduce road stability and shorten service life. In this study, a new method of road debonding detection based on the acoustic vibration method is proposed to address the needs of hidden debonding defects which are difficult to detect. The approach combines the Transformer model and the Transformer-based Parallel Cross-Gated Convolutional Neural Network (T-PCG-CNN) to classify and recognize semi-rigid base asphalt pavement acoustic data. Firstly, over a span of several years, an excitation device was designed and employed to collect acoustic data from different road types, creating a dedicated multi-sample dataset specifically for semi-rigid base asphalt pavements. Secondly, the improved Mel frequency cepstral coefficient (MFCC) feature and its first-order differential features (ΔMFCC) and second-order differential features (Δ2MFCC) are adopted as the input data of the network for different sample acoustic signal characteristics. Then, the proposed T-PCG-CNN model fuses the multi-frequency feature extraction advantage of a parallel cross-gate convolutional network and the long-time dependency capture ability of the Transformer model to improve the classification performance of different road acoustic features. Comprehensive experiments were conducted to analyze parameter sensitivity, feature combination strategies, and comparisons with existing classification algorithms. The results demonstrate that the proposed model achieves high accuracy and weighted F1 score. The confusion matrix indicates high per-class recall (including debonding), and the one-vs-rest ROC curves (AUC ≥ 0.95 for all classes) confirm strong class separability with low false-alarm trade-offs across operating thresholds. Moreover, the use of blockwise self-attention with global tokens and shared weight matrices significantly reduces model complexity and size. In the multi-type road data classification test, the classification accuracy reaches 0.9208 and the weighted F1 value reaches 0.9315, which is significantly better than the existing methods, demonstrating its generalizability in the identification of multiple road defect types. Full article
(This article belongs to the Section Civil Engineering)
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49 pages, 48189 KB  
Article
Prediction and Optimization of the Restoration Quality of University Outdoor Spaces: A Data-Driven Study Using Image Semantic Segmentation and Explainable Machine Learning
by Xiaowen Zhuang, Zhenpeng Tang, Shuo Lin and Zheng Ding
Buildings 2025, 15(16), 2936; https://doi.org/10.3390/buildings15162936 - 19 Aug 2025
Viewed by 528
Abstract
Evaluating the restoration quality of university outdoor spaces is often constrained by subjective surveys and manual assessment, limiting scalability and objectivity. This study addresses this gap by applying explainable machine learning to predict restorative quality from campus imagery, enabling large-scale, data-driven evaluation and [...] Read more.
Evaluating the restoration quality of university outdoor spaces is often constrained by subjective surveys and manual assessment, limiting scalability and objectivity. This study addresses this gap by applying explainable machine learning to predict restorative quality from campus imagery, enabling large-scale, data-driven evaluation and capturing complex nonlinear relationships that traditional methods may overlook. Using Fujian Agriculture and Forestry University as a case study, this study extracted road network data, generated 297 coordinates at 50-m intervals, and collected 1197 images. Surveys were conducted to obtain restorative quality scores. The Mask2Former model was used to extract landscape features, and decision tree algorithms (RF, XGBoost, GBR) were selected based on MAE, MSE, and EVS metrics. The combination of optimal algorithms and SHAP was employed to predict restoration quality and identify key features. This research also used a multivariate linear regression model to identify features with significant statistical impact but lower features importance ranking. Finally, the study also analyzed heterogeneity in scores for three restoration indicators and five campus zones using k-means clustering. Empirical results show that natural elements like vegetation and water positively affect psychological perception, while structural components like walls and fences have negative or nonlinear effects. On this basis, this study proposes spatial optimization strategies for different campus areas, offering a foundation for creating high-quality outdoor environments with restorative and social functions. Full article
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