Road Centerline Research Articles

Road Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression

Accurate road centerline extraction from remotely sensed images plays a significant role in road map generation and updating. In the road extraction problem, the acquisition of labeled data is time consuming and costly; thus, there are only a small amount of labeled samples in reality. In the existing centerline extraction algorithms, the thinning-based algorithms always produce small spurs that reduce the smoothness and accuracy of the road centerline; the regression-based algorithms can extract a smooth road network, but they are time consuming. To solve the aforementioned problems, we propose a novel road centerline extraction method, which is constructed based on semisupervised segmentation and multiscale filtering (MF) and multidirection nonmaximum suppression (M-NMS) (MF&M-NMS). Specifically, a semisupervised method, which explores the intrinsic structures between the labeled samples and the unlabeled ones, is introduced to obtain the segmentation result. Then, a novel MF&M-NMS-based algorithm is proposed to gain a smooth and complete road centerline network. Experimental results on a public data set demonstrate that the proposed method achieves comparable or better performances by comparing with the state-of-the-art methods. In addition, our method is nearly ten times faster than the state-of-the-art methods.

Tracking Road Centerlines from Remotely Sensed Imagery Using Mean Shift and Kalman Filtering

Tracking Road Centerlines from Remotely Sensed Imagery Using Mean Shift and Kalman Filtering

Multi-Criteria, Graph-Based Road Centerline Vectorization Using Ordered Weighted Averaging Operators

In this paper a novel road vectorization methodology based on image space clustering technique and weighted graph theory is presented. The proposed methodology describes a road as a set of optimized points on the centerline which should be connected by defining a number of appropriate criteria. The main contribution of this paper is to design a weighting scheme for combining a small number of road identities using Ordered Weighted Averaging (owa) operators by defining appropriate decision strategy. In this regard, a novel geometric criterion is introduced. Result of the OWA aggregation specifies weight of each edge in the road network graph. Comparing the proposed approach with two state-of-the-art image space clustering-based road vectorization methods proves its efficiency to deal with roads with different widths, parallel roads with different distances, different types of intersections, and also noise clusters. Obtaining improved quality measures for several high-resolution images, demonstrates the successfulness of the vectorization approach.

Automatic Extraction of Urban Road Centerlines from High-Resolution Satellite Imagery Using Automatic Thresholding and Morphological Operation Method

The commercial high-resolution imaging satellite with 1 m spatial resolution IKONOS is an important data source of information for urban planning and geographical information system (GIS) applications. In this paper, a morphological method is proposed. The proposed method combines the automatic thresholding and morphological operation techniques to extract the road centerline of the urban environment. This method intends to solve urban road centerline problems, vehicle, vegetation, building etc. Based on this morphological method, an object extractor is designed to extract road networks from highly remote sensing images. Some filters are applied in this experiment such as line reconstruction and region filling techniques to connect the disconnected road segments and remove the small redundant. Finally, the thinning algorithm is used to extract the road centerline. Experiments have been conducted on a high-resolution IKONOS and QuickBird images showing the efficiency of the proposed method.

Road vectorisation from high‐resolution imagery based on dynamic clustering using particle swarm optimisation

AbstractThis paper introduces an innovative automatic road‐vectorisation algorithm based on dynamic pixel clustering using particle swarm optimisation. A new cost function is designed to optimise the number and position of road keypoints and is capable of deriving road centrelines without considering geometric, spectral or topological characteristics in the road model. The algorithm is applied to different high‐resolution images (IKONOS, QuickBird and aerial photographs) and is evaluated with respect to RMSE, correctness and completeness. Moreover, a new quality parameter is defined to evaluate a “kinking” effect in roads. Extraction of different road shapes with an acceptable precision in both urban and rural environments proves the efficiency of the algorithm in yielding complete road networks.

Connectivity and physical activity: using footpath networks to measure the walkability of built environments

There is now a strong body of research that suggests that the form of the built environment can influence levels of physical activity, leading to an increasing interest in incorporating health objectives into spatial planning and regeneration. There have been a number of strands to this research, one of which has sought to develop objective measurements of the built environment using geographic information systems involving measures of connectivity and proximity to compare the relative ‘walkability’of different neighbourhoods. The development of the ‘walkability index’ has become a popular indicator of the spatial distribution of those built environment features that are considered to have the greatest positive influence on levels of physical activity. The success of this measure is built on its ability to capture succinctly correlates of physical activity using routinely available spatial data, which includes using road centre lines to measure connectivity. In this paper we discuss two key aspects of the walkability index. First, as suggested by others, that the use of a footpath network, rather than road centre lines, may be far more effective in evaluating walkability. This may be particularly important for assessing changes in walkability arising from pedestrian-focused infrastructure projects, such as greenways. Second, we explore the implications of this for how connectivity can be measured. We take six different measures of connectivity and first analyse the relationships between them and then test their correlation with actual levels of physical activity of local residents in Belfast, Northern Ireland. We find that the best measurements appear to be intersection density and metric reach and use this finding to discuss the implications for developing tools that may better support decision-making in spatial planning.

GIS를 이용한 미시적 수준의 교통모형 구현에 관한 연구

This study aims to design and implement a traffic model that can simulate the traffic behavior on the microscopic level by using the GIS. In the design of the model, the vehicle in the simulation environment recognizes the GIS road centerline data as road network data reflecting number of lanes, speed limit and so on. In addition, the behavior model was designed by dividing functions into the environmental perception model, time headway distribution model, car following model, and lane changing model. The implemented model was applied to Jahamun-road of Jongno-gu district to verify the accuracy of the model. As a result, the simulation results on the Jahamun-road had no great error compared with the actual observation data. In the aspect of usability of model, it is judged that this model will be able to effectively contribute to analysis of amount of carbon emission by traffic, evaluation of traffic flow, plans for location of urban infrastructure and so on.

Functional classification of the Gauteng provincial road network using the South African Road Classification and Access Management Manual (TRH26)

The built environment consists of various land uses and activities connected by a road network. The efficiency and effectiveness of the road network directly impacts economic growth and societal movement. This study involved the functional classification of the Gauteng provincial road network using the South African Road Classification and Access Management Manual (TRH26) as a guideline. In combination with high resolution up-to-date satellite reference imagery, dwelling and building inventories, road centrelines and other base layers, it was found that the TRH26 provided a well-structured approach to classifying the provincial road network according to the functional properties of each road link. It resulted in an accurate spatial representation of the current Gauteng provincial road network when using an appropriate geographic information system - providing a wealth of formal and technical information regarding each road and street via the comprehensive attribute database. At link level it was possible to synchronise the upgraded network with the financial asset registry in order to gauge the current state of the provincial roads within Gauteng, and to identify those that are in need of attention and/or rehabilitation to optimise efficiency and safety. This approach can be standardised throughout the country to make inferences to road safety, speed regulation, driver behaviour, traffic studies and accessibility to services.

A study of road centerline extraction from high resolution image

A study of road centerline extraction from high resolution image

A Method for Automatic Road Extraction of High Resolution SAR Imagery

Nowadays automatic road extraction from satellite imageries is considered as one of the most important research trends in the field of remote sensing. This paper presents a method for automatic extraction of road centerlines from syn- thetic aperture radar (SAR) imagery. During the first step, three features, namely the direction of the least total radiance, the corresponding radiance, and the contrast are extracted to definethe road characteristics bythe backscatter coefficient of each pixel and its neighboring pixels from the SAR imagery. The fusion of the extracted features is carried out in the next step for detection of the road areas by using a fuzzy inference system. Afterwards, the morphology skeletonization is ap- plied on the roadareas to extract the roadskeleton. Then some interested seed points are extracted so that they could be used in a snake model, which is employed to connect the seed points in order to form up the road centerlines. The proposed algorithm is tested on different parts of TerraSAR-X images. The experimental results reveal that the proposed method is effective in terms of correctness, completeness, and quality.

Spatial analysis of geometric design consistency and road sight distance

Because of the high number of crashes occurring on highways, it is necessary to intensify the search for new tools that help in understanding their causes. This research explores the use of a geographic information system (GIS) for an integrated analysis, taking into account two accident-related factors: design consistency (DC) (based on vehicle speed) and available sight distance (ASD) (based on visibility). Both factors require specific GIS software add-ins, which are explained. Digital terrain models (DTMs), vehicle paths, road centerlines, a speed prediction model, and crash data are integrated in the GIS. The usefulness of this approach has been assessed through a study of more than 500 crashes. From a regularly spaced grid, the terrain (bare ground) has been modeled through a triangulated irregular network (TIN). The length of the roads analyzed is greater than 100 km. Results have shown that DC and ASD could be related to crashes in approximately 4% of cases. In order to illustrate the potential of GIS, two crashes are fully analyzed: a car rollover after running off road on the right side and a rear-end collision of two moving vehicles. Although this procedure uses two software add-ins that are available only for ArcGIS, the study gives a practical demonstration of the suitability of GIS for conducting integrated studies of road safety.

Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image

Road damage detection and assessment from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pairing of pre-disaster and post-disaster road data for change detection and assessment is difficult to achieve due to the mismatch of different data sources, especially for rural areas where the pre-disaster data (i.e., remote sensing imagery or vector map) are hard to obtain. In this study, a knowledge-based method for road damage detection and assessment solely from post-disaster high-resolution remote sensing image is proposed. The road centerline is firstly extracted based on the preset road seed points. Then, features such as road brightness, standard deviation, rectangularity, and aspect ratio are selected to form a knowledge model. Finally, under the guidance of the road centerline, the post-disaster roads are extracted and the damaged roads are detected by applying the knowledge model. In order to quantitatively assess the damage degree, damage assessment indicators with their corresponding standard of damage grade are also proposed. The newly developed method is evaluated using a WorldView-1 image over Wenchuan, China acquired three days after the earthquake on 15 May 2008. The results show that the producer’s accuracy (PA) and user’s accuracy (UA) reached about 90% and 85%, respectively, indicating that the proposed method is effective for road damage detection and assessment. This approach also significantly reduces the need for pre-disaster remote sensing data.

Connected component-based technique for automatic extraction of road centerline in high resolution satellite images

In remote sensing analysis, automatic extraction of road network from satellite or aerial images can be a most needed approach for efficient road database creation, refinement, and updating. In this paper, a method for automatic extraction of road centerline from high resolution satellite image is proposed. The proposed work consists of three stages: segmentation of road region, connected component-based operations used to extract the connected road component from segmented road region, and removal of unwanted non-road pixels using morphological operation. The proposed algorithm is implemented on various satellite images, and the results are given in this work. The performance of the proposed method is evaluated by comparing the results with ground truth road map as reference data, and performance measures such as completeness, correctness, and quality are calculated. The average value of completeness, correctness, and quality of various images are 90%, 96%, and 87%, respectively. These measures prove that the proposed work yields road network very closer to reference road map. The proposed method yields very good result for noisy image also, and it proved that the proposed method is insensitive for noise. Performance measures of the proposed work are compared with other methods, and this comparison proves that the proposed method yields very good results than other methods.

ROAD AND ROADSIDE FEATURE EXTRACTION USING IMAGERY AND LIDAR DATA FOR TRANSPORTATION OPERATION

Abstract. Transportation agencies require up-to-date, reliable, and feasibly acquired information on road geometry and features within proximity to the roads as input for evaluating and prioritizing new or improvement road projects. The information needed for a robust evaluation of road projects includes road centerline, width, and extent together with the average grade, cross-sections, and obstructions near the travelled way. Remote sensing is equipped with a large collection of data and well-established tools for acquiring the information and extracting aforementioned various road features at various levels and scopes. Even with many remote sensing data and methods available for road extraction, transportation operation requires more than the centerlines. Acquiring information that is spatially coherent at the operational level for the entire road system is challenging and needs multiple data sources to be integrated. In the presented study, we established a framework that used data from multiple sources, including one-foot resolution color infrared orthophotos, airborne LiDAR point clouds, and existing spatially non-accurate ancillary road networks. We were able to extract 90.25% of a total of 23.6 miles of road networks together with estimated road width, average grade along the road, and cross sections at specified intervals. Also, we have extracted buildings and vegetation within a predetermined proximity to the extracted road extent. 90.6% of 107 existing buildings were correctly identified with 31% false detection rate.

Automatic optimization of road network clustering based on PSO for road centerline extraction

Automatic optimization of road network clustering based on PSO for road centerline extraction

Effect of geocoding errors on traffic-related air pollutant exposure and concentration estimates.

Exposure to traffic-related air pollutants is highest very near roads, and thus exposure estimates are sensitive to positional errors. This study evaluates positional and PM2.5 concentration errors that result from the use of automated geocoding methods and from linearized approximations of roads in link-based emission inventories. Two automated geocoders (Bing Map and ArcGIS) along with handheld GPS instruments were used to geocode 160 home locations of children enrolled in an air pollution study investigating effects of traffic-related pollutants in Detroit, Michigan. The average and maximum positional errors using the automated geocoders were 35 and 196 m, respectively. Comparing road edge and road centerline, differences in house-to-highway distances averaged 23 m and reached 82 m. These differences were attributable to road curvature, road width and the presence of ramps, factors that should be considered in proximity measures used either directly as an exposure metric or as inputs to dispersion or other models. Effects of positional errors for the 160 homes on PM2.5 concentrations resulting from traffic-related emissions were predicted using a detailed road network and the RLINE dispersion model. Concentration errors averaged only 9%, but maximum errors reached 54% for annual averages and 87% for maximum 24-h averages. Whereas most geocoding errors appear modest in magnitude, 5% to 20% of residences are expected to have positional errors exceeding 100 m. Such errors can substantially alter exposure estimates near roads because of the dramatic spatial gradients of traffic-related pollutant concentrations. To ensure the accuracy of exposure estimates for traffic-related air pollutants, especially near roads, confirmation of geocoordinates is recommended.

Use of GMM and SCMS for Accurate Road Centerline Extraction from the Classified Image

The extraction of road centerline from the classified image is a fundamental image analysis technology. Common problems encountered in road centerline extraction include low ability for coping with the general case, production of undesired objects, and inefficiency. To tackle these limitations, this paper presents a novel accurate centerline extraction method using Gaussian mixture model (GMM) and subspace constraint mean shift (SCMS). The proposed method consists of three main steps. GMM is first used to partition the classified image into several clusters. The major axis of the ellipsoid of each cluster is extracted and deemed to be taken as the initial centerline. Finally, the initial result is adjusted using SCMS to produce precise road centerline. Both simulated and real datasets are used to validate the proposed method. Preliminary results demonstrate that the proposed method provides a comparatively robust solution for accurate centerline extraction from a classified image.

국가공간정보통합체계 품질관리시스템 구축을 위한 BPR 수립

Abstract In Korea, National spatial data infrastructure has implemented in order to integrated manage and share the national spatial information producted by public agencies and local governments. The necessities of systematic quality management are raised, because information, which is generated by different agencies, is integrative managed by national level. In this paper, the establishment of BPR(Business Process Reengineering) for national spatial data infrastructure quality management system. Quality management business is defined as quality management object definition, quality measuring, evaluation and analysis, and quality enhancement process. Next, activities for each process are designed. For the quality management business, business rule(BR) is required for determining error. We derive the BR for six objects(legal-dong, railway boundary, railway centerline, road boundary, road centerline, building) among the basic spatial information. Other information's BR can be generated by using the derivation method described in this paper. Based on the BPR of this paper and derived BR, national spatial data infrastructure quality management system can be implemented in the future. Keywords: Business Process Reengineering, Spatial Information, Quality Management, Business Rule

Matching Road Network Combining Hierarchical Strokes and Probabilistic Relaxation Method

Aiming at the complex multisource road network matching modes, this paper proposed a road network match- ing method based on hierarchical stable strokes. The progress of the matching method adopts recursive method and gives consideration to both global consistency and local similarity of homonymous road features. One layer of hierarchical strokes generating from road spatial structure are selected at a match and the matched parts are used as a stable reference for the next layer. The detailed matching is implemented by an iterative probability relaxation method. In the course of coarse matching three similarity indicators, including the geometry shape similarity of strokes, the relative position of nodes and strokes and the topological structure similarity of nodes, are introduced into the initial matching probability calculation. After that, an iterative calculation is carried out for updating matching probability. Finally, a self-designed se- lecting principle is applied to trace the exact matching results. Matching road centerline from different producers of the same geographical area shows that our method finds 1:1, 1:N and M:N matching modes and gets a satisfactory matching result, which means hierarchical stable reference and the stroke structure can provide an effective way to identify different matching relations and avoid the non-rigid deviation of the homonymous features.

A Method for Accurate Road Centerline Extraction From a Classified Image

Accurate road centerline extraction plays an important role in practical remote sensing applications. Most existing centerline extraction methods have many limitations when the classified image contains complicated objects such as curvilinear, close, or short extent features. To cope with these limitations, this study presents a novel accurate centerline extraction method that integrates tensor voting, principal curves, and the geodesic method. The proposed method consists of three main steps. Tensor voting is first used to extract feature points from the classified image. The extracted feature points are then projected onto the principal curves. Finally, the feature points are linked by the geodesic method to create the central line. The experimental results demonstrate that the proposed method, which is automatic, provides a comparatively accurate solution for centerline extraction from a classified image.