Road Centerline Research Articles

Next generation gravel road profiling – The potential of advanced UAV drone in comparison with road surface tester and rotary laser levels

Over the last decades, significant progress has been made and new approaches have been proposed for efficient collection of road condition data. Gravel roads are crucial for connecting urban and rural areas in Sweden, constituting a significant portion of the road network. Therefore, this study addresses the use of a developed Unmanned Aerial Vehicle (UAV)-based digital imaging system focusing on efficient collection of surface condition data over gravel roads.The study focuses on in-situ profile measurements of a gravel road located in Trosa, Sweden, using three different profiling methods: UAV drone with RTK technology, Road Surface Tester (RST), and Rotary Laser Level (RLL) to explore the agreement between these methods.The UAV drone, equipped with Real-Time Kinematic (RTK) technology, captures high-resolution images to produce detailed 3D surface models, overcoming the challenges posed by adverse weather conditions. Notable outcomes reveal RTK technology's stability, maintaining a steady 3D position accuracy below 2 cm. To enhance synchronization and comparison between different profiling methods, efforts should be made to standardize coordinate systems and measurement analysis software.Minimum average absolute differences of 1.1 cm, 1 cm, and 0.7 cm were recorded for all profiles (from 1 m left to 1 m right of the road centerline) in the comparisons between UAV drone – RST, UAV drone – RLL, and RST – RLL methods, respectively. This underlines the significant advancement in UAV drone technology, enabling remarkably accurate measurements of vertical offsets for profiling the tested gravel road despite the high altitude at which the UAV drone operates.

A dataset of the 1 km-grid road network density in China (2019)

Road network density is a basic index of evaluating regional road development scale and road network development degree. The measurement and monitoring of road network density (as a comprehensive characterization index) plays an important role in the assessment of regional traffic and economic development, and provide reference for urban road management and control measures, which is of great significance to urban planning and municipal management. In this paper, based on the vector data of railway land, highway land, urban and village road land and rail transit in the Third National Land Survey, we extracted the road center line, using the km-grade network production software, to obtain a dataset of the 1km-grid road network density in China in 2019. Rigorous quality control and quality inspection measures throughout all stages of data processing have been implemented, including road surface vector extraction, road skeleton line extraction, and kilometer grid density calculation, to ensure the correctness, integrity and consistency of the data. This dataset can be used for the study on the preparation for the national territory development plan, the evaluation of plan implementation, the calculation of status factors for regional transportation development, and accessibility calculation.

Urban road waterlogging multi-level assessment integrated flood models and road network models

Road centerline and maximum inundation depth methods have been used in most previous studies to assess road waterlogging, but accurately expressing the spatiotemporal characteristics of road waterlogging is difficult. To address this issue, a new multilevel road waterlogging assessment model based on four spatial scales and four assessment indices was developed. Then, a road waterlogging information acquisition method was designed based on flood models (FMs) and spatial analysis methods, and existing multihierarchical road network models (RNMs) were extended to integrate road waterlogging information. Next, with a typical region selected as the study area, road waterlogging information under specific rainstorm scenarios was acquired and analyzed with the FMs and RNMs. Then, a multilevel assessment of road waterlogging and various analyses were conducted. The case study showed that compared to traditional models, the new model improved the prediction of the spatiotemporal characteristics of road waterlogging.

Research on Improved Road Visual Navigation Recognition Method Based on DeepLabV3+ in Pitaya Orchard

Traditional DeepLabV3+ image semantic segmentation methods face challenges in pitaya orchard environments characterized by multiple interference factors, complex image backgrounds, high computational complexity, and extensive memory consumption. This paper introduces an improved visual navigation path recognition method for pitaya orchards. Initially, DeepLabV3+ utilizes a lightweight MobileNetV2 as its primary feature extraction backbone, which is augmented with a Pyramid Split Attention (PSA) module placed after the Atrous Spatial Pyramid Pooling (ASPP) module. This improvement enhances the spatial feature representation of feature maps, thereby sharpening the segmentation boundaries. Additionally, an Efficient Channel Attention Network (ECANet) mechanism is integrated with the lower-level features of MobileNetV2 to reduce computational complexity and refine the clarity of target boundaries. The paper also designs a navigation path extraction algorithm, which fits the road mask regions segmented by the model to achieve precise navigation path recognition. Experimental findings show that the enhanced DeepLabV3+ model achieved a Mean Intersection over Union (MIoU) and average pixel accuracy of 95.79% and 97.81%, respectively. These figures represent increases of 0.59 and 0.41 percentage points when contrasted with the original model. Furthermore, the model’s memory consumption is reduced by 85.64%, 84.70%, and 85.06% when contrasted with the Pyramid Scene Parsing Network (PSPNet), U-Net, and Fully Convolutional Network (FCN) models, respectively. This reduction makes the proposed model more efficient while maintaining high segmentation accuracy, thus supporting enhanced operational efficiency in practical applications. The test results for navigation path recognition accuracy reveal that the angle error between the navigation centerline extracted using the least squares method and the manually fitted centerline is less than 5°. Additionally, the average deviation between the road centerlines extracted under three different lighting conditions and the actual road centerline is only 2.66 pixels, with an average image recognition time of 0.10 s. This performance suggests that the study can provide an effective reference for visual navigation in smart agriculture.

A Hierarchical Trajectory Planning Algorithm for Automated Guided Vehicles in Construction Sites

Herein, to address the challenges faced by Automatic Guided Vehicles (AGVs) in construction site environments, including heavy vehicle loads, extensive road search areas, and randomly distributed obstacles, this paper presents a hierarchical trajectory planning algorithm that combines coarse planning and precise planning. In the first-level coarse planning, lateral and longitudinal sampling is performed based on road environment constraints. A multi-criteria cost function is designed, taking into account factors such as deviation from the road centerline, shortest path cost, and obstacle collision safety cost. An efficient dynamic programming algorithm is used to obtain the optimal path. Considering nonholonomic constraints of vehicles, eliminating inflection points using improved B-Spline path fitting, and a quadratic programming algorithm is proposed to enhance path smoothness, completing the coarse planning algorithm. In the second-level precise planning, the coarse planning path is used as a reference line, and small-range sampling is conducted based on AGV motion constraints, including lateral displacement and longitudinal velocity. Lateral and longitudinal polynomials are constructed. To address the impact of randomly appearing obstacles on vehicle stability and safety, an evaluation function is designed, considering factors such as jerk and acceleration. The optimal trajectory is determined through collision detection, ensuring both safe obstacle avoidance and AGV smoothness. Experimental results demonstrate the effectiveness of this method in solving the path planning challenges faced by AGVs in construction site environments characterized by heavy vehicle loads, extensive road search areas, and randomly distributed obstacles.

RESEARCH ON SPATIAL CONSISTENCY CHECKING ACCORDING TO NATIONAL TECHNICAL REGULATION ON BASIC GEOGRAPHIC INFORMATION

Spatial consistency checking identifies logical inconsistencies according to pre-specified spatial rules, including data omission or commission, data overlap, and spatial connection. This research experimentally checks the consistency of these components according to national technical regulations on basic geographic information and the construction of checking rules based on GIS analysis methods to determine the relationship between geometric objects within a layer and between two different layers. In experiments using the ArcGIS software suite on some typical feature classes of the transportation group of the uncalibrated and uncorrected geographic database of the Bien Hoa town area at a scale of 1:5,000, it was shown that: the largest percentage of errors is the relationship between the traffic bridge (CauGiaoThong) layer and the road centerline (DoanTimDuongBo), 14.22%; next is the error of duplicate data of the road network node feature class (NutMangDuongBo), at 12.11%; then the relationship between the road surface and the road boundary, at 3.52; and the error of dangles of the road centerline, at 0.97%.

A heuristic-action-involved safe lane-change of autonomous vehicles with multiple constraints on road centerline and speed under highway environment

Lane-change (LC) is one of the most important topics in autonomous vehicles (AVs) on highways. To enhance the implementation of effective LC in AVs, this paper proposes a framework based on deep reinforcement learning, which takes into account heuristic actions and multiple constraints related to the centerline of the road and speed, to improve the overall performance of LC in AVs. Firstly, the influence of unreasonable vehicle actions on the algorithm training process is studied. To improve the rationality of the to-be-trained actions, a novel reasonable action screening mechanism is proposed. Secondly, to keep the vehicle on the centerline of the lane and avoid the collision with other vehicles, a method is designed to calculate the center position of the vehicle. Thirdly, a segmented speed reward mechanism is proposed to constrain vehicle speed. Subsequently, a dynamic reward function is established to train the control algorithm. Lastly, the proposed strategy is evaluated in two simulation scenarios of highways. The simulation results show that the proposed method can increase the number of reasonable actions by more than 30% and improve the success rate of obstacle avoidance with the increase of over 52% in both static and dynamic scenarios compared with the benchmark algorithms.

Intelligent Vehicle Decision-Making and Trajectory Planning Method Based on Deep Reinforcement Learning in the Frenet Space.

The complexity inherent in navigating intricate traffic environments poses substantial hurdles for intelligent driving technology. The continual progress in mapping and sensor technologies has equipped vehicles with the capability to intricately perceive their exact position and the intricate interplay among surrounding traffic elements. Building upon this foundation, this paper introduces a deep reinforcement learning method to solve the decision-making and trajectory planning problem of intelligent vehicles. The method employs a deep learning framework for feature extraction, utilizing a grid map generated from a blend of static environmental markers such as road centerlines and lane demarcations, in addition to dynamic environmental cues including vehicle positions across varied lanes, all harmonized within the Frenet coordinate system. The grid map serves as the input for the state space, and the input for the action space comprises a vector encompassing lane change timing, velocity, and vertical displacement at the lane change endpoint. To optimize the action strategy, a reinforcement learning approach is employed. The feasibility, stability, and efficiency of the proposed method are substantiated via experiments conducted in the CARLA simulator across diverse driving scenarios, and the proposed method can increase the average success rate of lane change by 6.8% and 13.1% compared with the traditional planning control algorithm and the simple reinforcement learning method.

ENHANCING URBAN VEHICULAR NAVIGATION: IMPROVING CLASSICAL TOPOLOGICAL MAP MATCHING THROUGH RAY-CASTING

Abstract. Navigation is of paramount importance for land vehicles as it enables efficient and accurate movement from one location to another. Whether it is for personal navigation, commercial transportation, or emergency services, reliable navigation systems play a crucial role in ensuring safety, optimizing routes, and enhancing overall operational efficiency. This paper presents the integration of classical Topological Map Matching (TMM) with the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS), addressing the limitations of relying solely on road centerlines. A novel solution is proposed, leveraging the ray-casting algorithm to determine the predicted position’s area and employing a two-stage kinematic update process for enhanced positioning accuracy. The solution’s efficacy is evaluated through tests conducted on simulated GNSS outages within a road experiment conducted in the City of Toronto, demonstrating substantial improvements compared to the classical TMM approach. Notably, the proposed method achieved a considerable 82.33% reduction in RMS positioning error and a 33.71% improvement in maximum positioning error during the longest GNSS outage. By overcoming the limitations of classical TMM algorithms, this research contributes to the advancement of navigation and tracking systems, with future work focusing on practical implementations and optimization for diverse navigation scenarios.

General Optimal Trajectory Planning: Enabling Autonomous Vehicles with the Principle of Least Action

This study presents a general optimal trajectory planning (GOTP) framework for autonomous vehicles (AVs) that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently. Firstly, we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline. Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve. Considering the road constraints and vehicle dynamics, limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system. Furthermore, in selecting the optimal trajectory, we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’ behavior and summarizing their manipulation characteristics of “seeking benefits and avoiding losses.” Finally, by integrating the idea of receding-horizon optimization, the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility, optimality, and adaptability. Extensive simulations and experiments are performed, and the results demonstrate the framework’s feasibility and effectiveness, which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants. Moreover, we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’ manipulation.

A movement and interaction model for cyclists and other non-lane-based road users

Cyclists and other types of road users who do not adhere to lane discipline pose a challenge in microscopic traffic simulation. In most software, the models are adapted to increase the lateral flexibility of road users, either through introducing sub-lanes (SUMO) or introducing a continuous lateral axis (PTV Vissim). These solutions enable the simulation of some behaviors, such as passing within the same driving lane. However, other behaviors exhibited by these flexible road users, including switching between cycling infrastructure, the roadways, and the sidewalk, riding against the given direction of travel, and selecting unexpected pathways to cross at intersections, remain difficult to simulate. This paper presents a modeling approach for cyclists, users of micro-mobility modes, and other non-lane-based road users. This method uses the concept of guidelines, or desire lines, that represent the intended path of non-lane-based road users. Guidelines are the same in form as the center line of road (sub-)lanes. Instead of following these lines precisely, the guideline is used to determine the desired direction for the road user in the next time step, which is used as input into an adapted social force type model. The movement and interaction model is formulated based on the NOMAD model for pedestrian dynamics. The single acceleration vector is divided into a speed component and a direction component that are calibrated and validated using trajectory data from cyclists at four signalized intersections in Munich, Germany. Maximum Likelihood Estimation (MLE) is used to estimate the model parameters and k-fold cross-validation is used to evaluate the modeling approach. The results are discussed and an outlook for future research is presented.

Integrating Multi-Point Geostatistics, Machine Learning, and Image Correlation for Characterizing Positional Errors in Remote-Sensing Images of High Spatial Resolution

Remote-sensing images of high spatial resolution (HSR) are valuable sources of fine-grained spatial information for various applications, such as urban surveys and governance. There is continuing research on positional errors in remote-sensing images and their impacts in geoprocessing and applications. This paper explores the combined use of multi-point geostatistics (MPS), machine learning—in particular, generalized additive modeling (GAM)—and computer-image correlation for characterizing positional errors in images—in particular, HSR images. These methods are employed because of the merits of MPS in being flexible for non-parametric and joint simulation of positional errors in X and Y coordinates, the merits of GAM in being capable of handling non-stationarity in-positional errors through error de-trending, and the merits of computer-image correlation in being cost-effective in furnishing the training data (TD) required in MPS. Procedurally, image correlation is applied to identify homologous image points in reference-test image pairs to extract image displacements automatically in constructing TD. To cope with the complexity of urban scenes and the unavailability of truly orthorectified images, visual screening is performed to clean the raw displacement data to create quality-enhanced TD, while manual digitization is used to obtain reference sample data, including conditioning data (CD), for MPS and test data for performance evaluation. GAM is used to decompose CD and TD into trends and residuals. With CD and TD both de-trended, the direct sampling (DS) algorithm for MPS is applied to simulate residuals over a simulation grid (SG) at 80 m spatial resolution. With the realizations of residuals and, hence, positional errors generated in this way, the means, standard deviation, and cross correlation in bivariate positional errors at SG nodes are computed. The simulated error fields are also used to generate equal-probable realizations of vertices that define some road centerlines (RCLs), selected for this research through interpolation over the aforementioned simulated error fields, leading to error metrics for the RCLs and for the lengths of some RCL segments. The enhanced georectification of the RCLs is facilitated through error correction. A case study based in Shanghai municipality, China, was carried out, using HSR images as part of generalized point clouds that were developed. The experiment results confirmed that by using the proposed methods, spatially explicit positional-error metrics, including means, standard deviation, and cross correlation, can be quantified flexibly, with those in the selected RCLs and the lengths of some RCL segments derived easily through error propagation. The reference positions of these RCLs were obtained through error correction. The positional accuracy gains achieved by the proposed methods were found to be comparable with those achieved by conventional image georectification, in which the CD were used as image-georectification control data. The proposed methods are valuable not only for uncertainty-informed image geolocation and analysis, but also for integrated geoinformation processing.

Trajectory-Based Method for Dividing Lanes of Vehicle Trajectories Collected by Roadside LiDAR

The detailed vehicle trajectories collected by various advanced sensors (e.g., LiDARs, radars, digital cameras) form an essential foundation for in-depth analysis of connected and automated transportation systems, in which lane division of vehicle trajectories is an essential step. Rather than high-definition maps, trajectory-based methods have attracted increasing attention for their merits of low cost and adaptability to map changes in a timely manner. However, they require a priori geometric information about roads which is usually unavailable in practice. To address this problem, we propose a method solely based on trajectory data to derive geometric road features for dividing lanes of trajectories collected in connected environments. The proposed method consists of two stages: (i) to extract the lane lines of the road, the direction of the road centerline is calculated as the weighted arithmetic average of trajectory directions, and an iterative process can keep updating the feature points of road centerline and lane lines based on kernel density estimation of trajectories; (ii) trajectories are divided into lanes by constructing a polygon based on the extracted lane lines. We conducted a field experiment to collect high-precision vehicle trajectories using roadside LiDAR devices and created a high-definition digital map at an urban road without signalized intersection in the city of Chengdu. The experiment based on the empirical data demonstrates the proposed method can accurately divide lanes for 97.83% of trajectory points. In addition, a series of sensitivity analyses show that the proposed method is robust to the noise in trajectory data and the error of calibrating the roadside LiDAR devices.

Damage behavior of extra-wide reinforced concrete box girder with multiple chambers under repeated explosive loads: Testing and numerical simulation

Explosive tests of scaled girder specimens and numerical simulations of prototype girder segments were implemented to study the dynamic response and failure characteristics of extra-wide reinforced concrete box girders subjected to repeated explosions. First, a segmental girder specimen was manufactured at a reduced scale of 1:3 based on the prototype girder of a real bridge. The damage behavior of the specimen was tested by detonating two 3-kg TNT (trinitrotoluene) grains in sequence at a height of 0.40 m above the center of the middle chamber. Second, LS-DYNA software was used to simulate the explosive response of the specimen. The reliability of the numerical simulation method was verified by comparing its results with test data. Finally, the effects of different TNT equivalents, detonation positions, and repeated explosion modes on the anti-explosion performance of the prototype girder segment are simulated. The results showed that the holes in the top plate of the middle chamber of the girder specimen were 41.50 cm × 45.50 cm and 56.80 cm × 63.50 cm along the longitudinal and transverse directions, respectively, under the two explosions. The concrete at the bottom of the top plate peeled off over a wide area, and cracks appeared in the bottom plate close to the supports. In comparing the simulated damage behavior of different girder segment types along the transverse direction, the dynamic response of the T-beam section segment was found to be greater than that of the box section segment under the same explosive charge. The maximum response was located at the road centerline. Moreover, the degree of damage to the girder was more severe under repeated explosions with a certain amount of charge compared with that under a single explosion with twice the amount of charge. The degree of damage to the web, bottom plate, and crossbeam caused by the explosions in sequence from outside to inside the box with two 200-kg TNT grains was more severe than that caused by a single explosion of 400-kg TNT grains. Simultaneous detonations at multiple positions severely degrade the bridge traffic capacity.

Road extraction from low-cost GNSS-device dense trajectories

ABSTRACT This paper proposes a method for road centerline extraction from dense Global Navigation Satellite System (GNSS) trajectories, collected by using low-cost GNSS-devices, i.e. smartphones. The proposed method basically consists in generating a frequency image by tracking the GNSS trajectories and then by applying the Steger line detector to the generated image to extract the road centerlines. The main motivation of using the Steger algorithm is its capability to detect lines with sub-pixel accuracy. To evaluate the obtained results, reference road centerlines are manually extracted from a georeferenced orthomosaic. The experiments performed demonstrate the high potential of applying the Steger line detector to frequency images, generated by using dense GPS (Global Positioning System) trajectories. The completeness and correctness values for the accomplished experiments were 98% and 99%, respectively. Additionally, the RMSE (Root Mean Square Error) ranged from 0.63 m to 2.39 m, or approximately 1/16 to 1/4 of the expected accuracy (about 10 m) of a point determined by the Single-Point Positioning (SPP) method, which is the GNSS positioning method usually employed by smartphones.

Updating forest road networks using single photon LiDAR in northern Forest environments

Abstract Knowledge about the condition and location of forest roads is important for forest management. Coupling accurate forest road information with planning and conservation strategies supports forest resource management. In Canada, spatial data of forestry road networks are available provincially; however, they lack spatial accuracy, and up-to-date information on key attributes such as road width is missing. In this study, we apply a novel approach to update forest road networks and characterize road conditions in Ontario’s Boreal and Great Lakes—St. Lawrence (GLSL) Forest regions. We use airborne laser scanning (ALS), to facilitate the identification of forest roads across densely forested landscapes. We categorized roads into four classes based on driveable width, edge vegetation, as well as surface and edge degradation as derived from high-density Single Photon LiDAR (SPL) data. Using a novel road extraction method, we produced a road probability raster and map road centerlines. We validated road location and attribute information using Global Navigation Satellite System (GNSS) ground truth data in two Ontario forest management units, in the boreal forest and the GLSL. Road segments in some regions have been altered to account for land cover changes, such as flooding or fallen trees. In other situations, the road path may deviate from the planned layout of the road, which is not always followed in the field. Our results highlight inaccuracies in the existing road networks, with 30 per cent of ‘Full access’ roads and 29 per cent of ‘Partial access’ roads being undriveable by standard vehicles and 45 per cent of ‘Status unknown’ roads, which make up 48 per cent of the pre-existing network, being driveable by standard vehicles. Results show that the average positional accuracy of updated road centerlines is 0.4 m, and the average road width error is 2 m. The production of spatially accurate forest road networks and road attribute information is important for characterizing large road networks for which often minimal information is available.

Evaluation and analysis of tunnel lighting service status based on driving safety

Objective The service status of tunnel lighting has an important impact on driving safety. Existing research mainly discussed the impact of a single evaluation indicator of lighting service status on driving safety and paid less attention to the impact of multiple evaluation indicators on driving safety. Methods In this research, field tests of tunnels in three speed scenarios and 52 service scenarios are completed. DIALux software was selected to simulate more intensive illuminance, which could be used to calculate U0 , U1 , qc , and C. Using U0 , U1 , qc , and C as explanatory variables, and applying the normalized visual recognition results as explained variables, ordinary least squares regression (OLS) was performed to calculate the influence weight of each explanatory variable on driving safety. Results The results show that the four independent variables occupied different weights under different working conditions. However, on the whole, U0 and U1 occupied a greater weight. The weight of C lies between qc and U0 , and U1 and qc occupied the smallest weight in all scenarios. Locally, U0 was greater than U1 , with the exception when the illumination attenuation value was 70%. Conclusions The influence of the total uniformity of the road surface luminance and longitudinal uniformity of the road centerline luminance on driving safety was significantly greater than the influence of the contrast revealing coefficient and luminance contrast C on driving safety. When the luminous flux was attenuated to 70% or less and when the number of failed lamps was at least four, the lighting quality would be significantly reduced, which would substantially affect driving safety.

Influence of depressed road configuration on downwind pollutant concentrations: A CFD study under various thermal stability conditions

Outdoor air quality is a major concern worldwide, especially in urban areas. In this paper, the influence of depressed roads (specific road designs where the road's surface is lower than the surrounding ground level) on the downwind pollutant concentration was assessed using a validated Computational Fluid Dynamics (CFD) solver, for eight road depths (D) and thirteen stability conditions (Richardson numbers, Ri). Depressed roads can decrease downwind pollutant concentrations compared to classical roads, but only under neutral and unstable thermal conditions. Under neutral thermal condition, a threshold is reached for D = 0.375, leading to a maximal pollution reduction of around 10% at pedestrian level and around 5% at the first-floor level. In such stability conditions, pollutant concentrations are lower as D increases and Ri decreases. Under stable conditions, such roads lead to higher pollutant concentrations. Four equations allowing to predict the downwind pollutant concentrations are given depending on the distance from the road centerline, the road depth, and the thermal stability condition. The results of this study provide pre-construction guidance on whether a depressed road should be considered to protect human health, as well as predictive tools to assess the beneficial or adverse impact of such structures on air quality.

Framework for Geometric Information Extraction and Digital Modeling from LiDAR Data of Road Scenarios

Road geometric information and a digital model based on light detection and ranging (LiDAR) can perform accurate geometric inventories and three-dimensional (3D) descriptions for as-built roads and infrastructures. However, unorganized point clouds and complex road scenarios would reduce the accuracy of geometric information extraction and digital modeling. There is a standardization need for information extraction and 3D model construction that integrates point cloud processing and digital modeling. This paper develops a framework from semantic segmentation to geometric information extraction and digital modeling based on LiDAR data. A semantic segmentation network is improved for the purpose of dividing the road surface and infrastructure. The road boundary and centerline are extracted by the alpha-shape and Voronoi diagram methods based on the semantic segmentation results. The road geometric information is obtained by a coordinate transformation matrix and the least square method. Subsequently, adaptive road components are constructed using Revit software. Thereafter, the road route, road entity model, and various infrastructure components are generated by the extracted geometric information through Dynamo and Revit software. Finally, a detailed digital model of the road scenario is developed. The Toronto-3D and Semantic3D datasets are utilized for analysis through training and testing. The overall accuracy (OA) of the proposed net for the two datasets is 95.3 and 95.0%, whereas the IoU of segmented road surfaces is 95.7 and 97.9%. This indicates that the proposed net could accomplish superior performance for semantic segmentation of point clouds. The mean absolute errors between the extracted and manually measured geometric information are marginal. This demonstrates the effectiveness and accuracy of the proposed extraction methods. Thus, the proposed framework could provide a reference for accurate extraction and modeling from LiDAR data.

Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System

To effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape’s complexity, mainly due to severe shadows cast by the wild vegetation and trees, makes it challenging to extract rural roads based on processing aerial or satellite images, leading to heterogeneous results. This article proposes a method to improve the automatic detection of rural roads and the extraction of their centerlines from aerial images. This method has two main stages: (i) the use of a deep learning model (DeepLabV3+) for predicting rural road segments; (ii) an optimization strategy to improve the connections between predicted rural road segments, followed by a morphological approach to extract the rural road centerlines using thinning algorithms, such as those proposed by Zhang–Suen and Guo–Hall. After completing these two stages, the proposed method automatically detected and extracted rural road centerlines from complex rural environments. This is useful for developing real-time mapping applications.