Search Results (49)

Using Shanghai as a case study, this paper develops a multi-source fusion and interpretable machine learning framework. Sentiment indices were extracted from Weibo check-ins with ERNIE 3.0, street-view elements were identified using Mask2Former, and urban indicators like the Normalized Difference Vegetation Index, floor area ratio, and road network density were integrated. The coupling between residents’ sentiments and streetscape features during heatwaves was analyzed with Extreme Gradient Boosting, SHapley Additive exPlanations, and GeoSHAPLEY. Results show that (1) the average sentiment index is 0.583, indicating a generally positive tendency, with sentiments clustered spatially, and negative patches in central areas, while positive sentiments are concentrated in waterfronts and green zones. (2) SHapley Additive exPlanations analysis identifies NDVI (0.024), visual entropy (0.022), FAR (0.021), road network density (0.020), and aquatic rate (0.020) as key factors. Partial dependence results show that NDVI enhances sentiment at low-to-medium ranges but declines at higher levels; aquatic rate improves sentiment at 0.08–0.10; openness above 0.32 improves sentiment; and both visual entropy and color complexity show a U-shaped relationship. (3) GeoSHAPLEY shows pronounced spatial heterogeneity: waterfronts and the southwestern corridor have positive effects from water–green resources; high FAR and paved surfaces in the urban area exert negative influences; and orderly interfaces in the vitality corridor generate positive impacts. Overall, moderate greenery, visible water, openness, medium-density road networks, and orderly visual patterns mitigate negative sentiments during heatwaves, while excessive density and hard surfaces intensify stress. Based on these findings, this study proposes strategies: reducing density and impervious surfaces in the urban area, enhancing greenery and quality in waterfront and peripheral areas, and optimizing urban–rural interfaces. These insights support heat-adaptive and sustainable street design and spatial governance. Full article

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

Comprehensive evaluation of suburban landscape perception is essential for improving environmental quality and fostering integrated urban–rural development. Despite its importance, limited research has systematically extracted local visual features and analyzed influencing factors in suburban landscapes using multi-source data and machine learning. This study investigated Chongming District, a suburban area of Shanghai. Using Baidu Street View 360° panoramic images, local visual features were extracted through semantic segmentation of street view imagery, spatial multi-clustering, and random forest classification. A geographic detector model was employed to explore the relationships between landscape characteristics and their driving factors. The findings of the study indicate (1) significant spatial variations in the green visibility, sky openness, building density, road width, facility diversity, and enclosure integrity; (2) an intertwined spatial pattern of blue, green, and gray spaces; (3) the emergence of natural environment dimension factors as the primary drivers influencing the spatial configuration. In the suburban industrial dimension, the interaction between the GDP and commercial vitality exhibits the highest level of synergy. Based on these findings, targeted strategies are proposed to enhance the distinctive landscape features of Chongming Island. This research framework and methodology are specifically applied to Chongming District as a case study. Future studies should consider modifying the algorithms and index systems to better reflect other study areas, thereby ensuring the validity and precision of the results. Full article

Rural road programs are essential for enhancing connectivity in remote areas, yet they frequently encounter schedule delays and budget overruns. This study explores the extent to which specific project characteristics influence these deviations in Colombian rural road contracts. A dataset comprising 229 projects was extracted from the national SECOP open-procurement platform and processed using the CRISP-DM protocol. Following the cleaning and coding of 14 project-level variables, statistical analyses were conducted using Spearman correlations, Kruskal–Wallis tests, and post-hoc Wilcoxon comparisons to identify significant bivariate relations I confirm I confirm I confirm hips. A Random Forest model was subsequently applied to determine the most influential multivariate predictors of cost and time deviations. In parallel, a directed content analysis of contract addenda reclassified 22 recorded deviation descriptors into ten internationally recognized categories of causality, enabling an integrated interpretation of both statistical and documentary evidence. The findings indicate that contract value, geographical region, and contractor configuration are significant determinants of cost and time performance. Additionally, project intensity and discrepancies between awarded and bid values emerged as key contributors to cost escalation. Scope changes and adverse weather conditions together accounted for 76% of all documented deviation triggers, underscoring the relevance of robust front-end planning and climate-risk considerations in rural infrastructure delivery. The findings provide information for stakeholders, policymakers, and professionals who aim to manage the risk of schedule and budget deviations in public infrastructure projects. Full article

Traffic congestion constitutes a substantial global issue, adversely impacting economic productivity and quality of life, with associated costs estimated at approximately 2% of GDP in various nations. This systematic review investigates the application of machine learning (ML) in traffic congestion forecasting from 2010 to 2024, adhering to the PRISMA 2020 guidelines. A comprehensive search of three major databases (IEEE Xplore, SpringerLink, and ScienceDirect) yielded 9695 initial records, with 115 studies meeting the inclusion criteria following rigorous screening. Data extraction encompassed methodological approaches, ML techniques, traffic characteristics, and forecasting periods, with quality assessment achieving near-perfect inter-rater reliability (Cohen’s $\kappa$ = 0.89). Deep Neural Networks were the predominant technical approach (47%), with supervised learning being the most prevalent (57%). Classification tasks were the most common (42%), primarily addressing recurrent congestion scenarios (76%) and passenger vehicles (90%). The quality of publications was notably high, with 85% appearing in Q1-ranked journals, demonstrating exponential growth from minimal activity in 2010 to 18 studies in 2022. Significant research gaps persist: reinforcement learning is underutilized (8%), rural road networks are underrepresented (2%), and industry–academia collaboration is limited (3%). Future research should prioritize multimodal transportation systems, real-time adaptation mechanisms, and enhanced practical implementation to advance intelligent transportation systems (ITSs). This review was not registered because it focused on mapping the research landscape rather than intervention effects. Full article

The site selection and spatial arrangement of rural settlements embody the ethnic characteristics and cultural heritage of ethnic minority groups. Investigating their spatial layout features and underlying determinants can provide both theoretical foundations and practical methodologies for the conservation and development planning of these settlements. This paper takes the representative ethnic minority villages in the first batch of key traditional villages in Liuzhou, Guangxi, as the example, and employs a combination of qualitative and quantitative methods to study the spatial layout characteristics of ethnic minority villages in southern China. This study utilizes GIS-based analytical methods to calculate quantitative indicators based on planar graphs and digital elevation model (DEM) of ethnic minority settlements. The research results show that the spatial distribution of ethnic minority villages in southern China is closely correlated with natural geographical conditions. To be specific, ethnic minority villages in southern China generally distribute in accordance with the terrain and form specific spatial relationships with roads, topography, mountains, and water. Regardless of whether minority residents live on mountain tops or in valleys, they generally prefer gently sloping terrain. In addition, factors such as natural environment (sunshine and water sources, etc.) and traffic conditions are considered comprehensively in the settlement location. On this basis, the spatial layout features of rural settlement are extracted, and corresponding characteristic maps are constructed. The construction framework of the spatial characteristics map established based on “overall layout, architecture, roads and architecture-natural pattern” in this paper can be applied to general rural settlements. The findings can provide both theoretical foundations and practical references for the planning and development of rural settlements across different regions and ethnic groups. Full article

In response to the challenges of the low accuracy and high misdetection and omission rate of disease detection on feeder roads, an improved Rural-YOLO (SAConv-C2f+C2PSA_CAA+MCSAttention+WIOU) disease detection algorithm is proposed in this paper, which is an enhanced target detection framework based on the YOLOv11 architecture, for the identification of common diseases in the complex feeder road environment. The proposed methodology introduces four key innovations: (1) Switchable Atrous Convolution (SAConv) is introduced into the backbone network to enhance multiscale disease feature extraction under occlusion conditions; (2) Multi-Channel and Spatial Attention (MCSAttention) is constructed in the feature fusion process, and the weight distribution of multiscale diseases is adjusted through adaptive weight redistribution. By adjusting the weight distribution, the model’s sensitivity to subtle disease features is improved. To enhance its ability to discriminate between different disease types, Cross Stage Partial with Parallel Spatial Attention and Channel Adaptive Aggregation (C2PSA_CAA) is constructed at the end of the backbone network. (3) To mitigate category imbalance issues, Weighted Intersection over Union loss (WIoU_loss) is introduced, which helps optimize the bounding box regression process in disease detection and improve the detection of relevant diseases. Based on experimental validation, Rural-YOLO demonstrated superior performance with minimal computational overhead. Only 0.7 M additional parameters is required, and an 8.4% improvement in recall and a 7.8% increase in mAP₅₀ were achieved compared to the initial models. The optimized architecture also reduced the model size by 21%. The test results showed that the proposed model achieved 3.28 M parameters with a computational complexity of 5.0 GFLOPs, meeting the requirements for lightweight deployment scenarios. Cross-validation on multi-scenario public datasets was carried out, and the model’s robustness across diverse road conditions. In the quantitative experiments, the center skeleton method and the maximum internal tangent circle method were used to calculate crack width, and the pixel occupancy ratio method was used to assess the area damage degree of potholes and other diseases. The measurements were converted to actual physical dimensions using a calibrated scale of 0.081:1. Full article

Due to the imbalance between urban and rural development and improper management, the spatial forms of many heritage villages have suffered severe damage, and their landscape styles are gradually being blurred, posing serious challenges to the protection of traditional villages. Taking the traditional village of Xi Songbi in Jiexiu City, Shanxi Province, as a case study, this paper employs UAV low-altitude multi-view measurement technology to obtain high-resolution image data from different angles. Three-dimensional modeling technology is then used to construct a 3D real-world model, orthophotos, and point cloud data of the settlement. Based on these data, the weakly supervised point cloud segmentation method, DDLA, is further applied to finely segment and classify the acquired point cloud data, accurately extracting key spatial elements such as buildings, roads, and vegetation, thereby enabling a comprehensive and quantitative analysis of the spatial morphology of traditional villages. The results of the study show the following: (1) The use of UAVs for low-altitude multi-view measurement not only greatly improves the efficiency of data acquisition but also provides millimeter-level precision spatial data in a short time through the constructed 3D models and orthophotos. (2) The acquired point cloud data can be processed through the DDLA, which effectively differentiates building contours from other environmental elements. (3) The calculation and analysis of the segmented point cloud data can accurately quantify key spatial morphology elements, such as the dimensions of traditional village buildings, spacing, and road widths, ensuring the scientific rigor and reliability of the data. (4) The comprehensive application of digital technology and point cloud segmentation methods provides clear expectations and solid technical support for the quantitative study of the spatial morphology of traditional villages, laying a scientific foundation for the protection and sustainable development of cultural heritage. Full article

Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. Thorough pedestrian crash data are indispensable for safety research, as the most detailed descriptions of crash scenes and pedestrian actions are typically found in crash narratives and diagrams. However, extracting and analyzing this information from police crash reports poses significant challenges. This study tackles these issues by introducing innovative image-processing techniques to analyze crash diagrams. By employing cutting-edge technological methods, the research aims to uncover and extract hidden features from pedestrian crash data in Michigan, thereby enhancing the understanding and prevention of such incidents. This study evaluates the effectiveness of three Convolutional Neural Network (CNN) architectures—VGG-19, AlexNet, and ResNet-50—in classifying multiple hidden features in pedestrian crash diagrams. These features include intersection type (three-leg or four-leg), road type (divided or undivided), the presence of marked crosswalk (yes or no), intersection angle (skewed or unskewed), the presence of Michigan left turn (yes or no), and the presence of nearby residentials (yes or no). The research utilizes the 2020–2023 Michigan UD-10 pedestrian crash reports, comprising 5437 pedestrian crash diagrams for large urbanized areas and 609 for rural areas. The CNNs underwent comprehensive evaluation using various metrics, including accuracy and F1-score, to assess their capacity for reliably classifying multiple pedestrian crash features. The results reveal that AlexNet consistently surpasses other models, attaining the highest accuracy and F1-score. This highlights the critical importance of choosing the appropriate architecture for crash diagram analysis, particularly in the context of pedestrian safety. These outcomes are critical for minimizing errors in image classification, especially in transportation safety studies. In addition to evaluating model performance, computational efficiency was also considered. In this regard, AlexNet emerged as the most efficient model. This understanding is precious in situations where there are limitations on computing resources. This study contributes novel insights to pedestrian safety research by leveraging image processing technology, and highlights CNNs’ potential use in detecting concealed pedestrian crash patterns. The results lay the groundwork for future research, and offer promise in supporting safety initiatives and facilitating countermeasures’ development for researchers, planners, engineers, and agencies. Full article

Due to the rapid expansion of urban areas, the aging of agricultural labor, and the loss of rural workforce, some regions in China have experienced farmland abandonment. The use of remote sensing technology allows for the rapid and accurate extraction of abandoned farmland, which is of great significance for research on land-using change, food security protection, and ecological and environmental conservation. This research focuses on Qiaotou Town in Chengmai County, Hainan Province, as the study area. Using four high-resolution satellite imagery scenes, digital elevation models, and other relevant data, the random forest classification method was applied to extract abandoned farmland and analyze its spatial distribution characteristics. The accuracy of the results was verified. Based on these findings, the study examines the influence of four factors—irrigation conditions, slope, accessibility, and proximity to residential areas—on farmland abandonment and proposes corresponding governance policies. The results indicate that the accuracy of abandoned farmland extraction using high-resolution satellite imagery is 93.29%. The phenomenon of seasonal farmland abandonment is more prevalent than perennial farmland abandonment in the study area. Among the influencing factors, the abandonment rate decreases with increasing distance from road buffer zones, increases with greater distance from water systems, and decreases with increasing distance from residential areas. Most of the abandoned farmland is located in areas with gentler slopes, which have a relatively smaller impact on farmland abandonment. This study provides valuable references for the extraction of abandoned farmland and for analyzing the abandonment mechanisms in the study area, which have a profound impact on agricultural economic development and help to support the implementation of rural revitalization strategies. Full article

Road extraction from UAV-based remote sensing images in rural areas presents significant challenges due to the diverse and complex characteristics of rural roads. Additionally, acquiring UAV remote sensing data for rural areas is challenging due to the high cost of equipment, the lack of clear road boundaries requiring extensive manual annotation, and limited regional policy support for UAV operations. To address these challenges, we propose a transferable contextual network (TCNet), designed to enhance the transferability and accuracy of rural road extraction. We employ a Stable Diffusion model for data augmentation, generating diverse training samples and providing a new method for acquiring remote sensing images. TCNet integrates the clustered contextual Transformer (CCT) module, clustered cross-attention (CCA) module, and CBAM attention mechanism to ensure efficient model transferability across different geographical and climatic conditions. Moreover, we design a new loss function, the Dice-BCE-Lovasz loss (DBL loss), to accelerate convergence and improve segmentation performance in handling imbalanced data. Experimental results demonstrate that TCNet, with only 23.67 M parameters, performs excellently on the DeepGlobe and road datasets and shows outstanding transferability in zero-shot testing on rural remote sensing data. TCNet performs well on segmentation tasks without any fine-tuning for regions such as Burgundy, France, and Yunnan, China. Full article

Cropland serves as the most vital resource for agricultural production, while its security is primarily threatened by abandonment. Northeast Guangdong Province features a fragmented terrain and faces a significant issue of farmland abandonment. It is crucial to analyze the phenomenon of cropland abandonment to safeguard food security. However, due to limitations in data sources and attribution methods, previous studies struggled to comprehensively characterize the driving mechanisms of abandoned land. Using data from Sentinel time series remote-sensing images, we employed the land use change trajectory method to map cropland abandonment in Jiaoling County from 2019 to 2023. Furthermore, we proposed a novel analytical framework to quantify the influence pathways and interaction effects driving cropland abandonment. The results indicated that: (1) The overall accuracy of the abandoned land extraction was 79.6%. During the study period, the abandonment rate in Jiaoling County showed a trend of a “gradual rise followed by a sharp decline”, and the abandoned area reached its maximum in 2021. The abandonment phenomenon in the southeastern rural areas was serious and stubborn. (2) The slope has the greatest explanatory power for abandonment, followed by the total cultivated area, aggregation index of cropland, and distance to road. Each driving factor has a threshold effect. (3) Topography, location, and agriculture driving factors directly or indirectly affect the abandonment rate, with direct influences of 0.247, 0.255, and −0.256, respectively. The research findings offer valuable scientific guidance for managing abandoned land and deepen our understanding of its formation mechanisms. Full article

Horse-and-buggy transportation, vital for many rural communities and the Amish population, has been largely overlooked in safety research. This study examines the characteristics and injury severity of horse-and-buggy roadway crashes in Michigan’s rural areas. Detailed crash data are essential for safety studies, as crash scene descriptions are mainly found in narratives and diagrams. However, extracting and utilizing this information from traffic reports is challenging. This research tackles these challenges using image-processing and text-mining techniques to analyze crash diagrams and narratives. The study employs the AlexNet convolutional neural network (CNN) to identify and extract horse-and-buggy crashes, analyzing (2020–2023) Michigan UD-10 rural crash reports. Natural Language Processing (NLP) techniques also identified primary risk factors from crash narratives, analyzing single-word patterns (“unigrams”) and sequences of three consecutive words (“trigrams”). The findings emphasize the risks involved in horse-and-buggy interactions on rural roadways and highlight various contributing factors to the severity of these crashes, including distracted or careless actions by motorists, nighttime visibility issues, and failure to yield, especially by elderly drivers. This study suggests prioritizing horse-and-buggy riders in road safety and public health programs and recommends comprehensive measures that could significantly reduce crash incidence and severity, improving overall safety in Michigan’s rural areas, including better signage, driver education, and community outreach. Also, the study highlights the potential of advanced image-processing techniques in traffic safety research that could lead to more precise and actionable findings, enhancing road safety for all users. Full article

High-resolution remote sensing imagery, reaching meter or sub-meter levels, provides essential data for extracting and identifying road information. However, rural roads are often narrow, elongated, and have blurred boundaries, with textures that resemble surrounding environments such as construction sites, vegetation, and farmland. These features often lead to incomplete extraction and low extraction accuracy of rural roads. To address these challenges, this study introduces the RC-MSFNet model, based on the U-Net architecture, to enhance rural road extraction performance. The RC-MSFNet model mitigates the vanishing gradient problem in deep networks by incorporating residual neural networks in the downsampling stage. In the upsampling stage, a connectivity attention mechanism is added after dual convolution layers to improve the model’s ability to capture road completeness and connectivity. Additionally, the bottleneck section replaces the traditional dual convolution layers with a multi-scale fusion atrous convolution module to capture features at various scales. The study focuses on rural roads in the Xiong’an New Area, China, using high-resolution imagery from China’s Gaofen-2 satellite to construct the XARoads rural road dataset. Roads were extracted from the XARoads dataset and DeepGlobe public dataset using the RC-MSFNet model and compared with some models such as U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. Experimental results showed that: (1) The proposed method achieved precision (P), intersection over union (IOU), and completeness (COM) scores of 0.8350, 0.6523, and 0.7489, respectively, for rural road extraction in Xiong’an New Area, representing precision improvements of 3.8%, 6.78%, 7.85%, 2.14%, 0.58%, and 2.53% over U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. (2) The method excelled at extracting narrow roads and muddy roads with unclear boundaries, with fewer instances of omission or false extraction, demonstrating advantages in complex rural terrain and areas with indistinct road boundaries. Accurate rural road extraction can provide valuable reference data for urban development and planning in the Xiong’an New Area. Full article

The pursuit of the Sustainable Development Goals has highlighted rural electricity consumption patterns, necessitating innovative analytical approaches. This paper introduces a novel method for predicting rural electricity consumption by leveraging deep convolutional features extracted from satellite imagery. The study employs a pretrained remote sensing interpretation model for feature extraction, streamlining the training process and enhancing the prediction efficiency. A random forest model is then used for electricity consumption prediction, while the SHapley Additive exPlanations (SHAP) model assesses the feature importance. To explain the human geography implications of feature maps, this research develops a feature visualization method grounded in expert knowledge. By selecting feature maps with higher interpretability, the “black-box” model based on remote sensing images is further analyzed and reveals the geographical features that affect electricity consumption. The methodology is applied to villages in Xinxing County, Guangdong Province, China, achieving high prediction accuracy with a correlation coefficient of 0.797. The study reveals a significant positive correlations between the characteristics and spatial distribution of houses and roads in the rural built environment and electricity demand. Conversely, natural landscape elements, such as farmland and forests, exhibit significant negative correlations with electricity demand predictions. These findings offer new insights into rural electricity consumption patterns and provide theoretical support for electricity planning and decision making in line with the Sustainable Development Goals. Full article