Road Vector Research Articles

A comprehensive index model for real estate convenience based on multi-source data

The description information of the convenience of real estate is often qualitative and simple, and lacks comprehensive evaluation, so it is difficult to make horizontal comparison of the convenience of different real estate. Therefore, this paper proposes a comprehensive index model which uses multi-source data to measure the real estate convenience quantitatively. Based on the walking index, the model adds vector road network data, poi (point of interest) data and other multi-source data, and introduces the concept of walking score. Combined with the practical application of real estate, the model classifies the convenience facilities and calculates the walking distance between the convenience facilities and real estate quantitatively, so as to obtain a real number index to comprehensively measure the convenience of real estate. Finally, the experimental results based on Amap data prove the accuracy and effectiveness of the proposed model.

Automated 3D Road Boundary Extraction and Vectorization Using MLS Point Clouds

To meet the urgent demands in a wide range of geospatial applications, such as road management, intelligent transportation systems, road safety evaluation, and traffic accident analysis, automatic and accurate extraction of 3D roads and associated geometric parameters from point clouds is receiving wide attention. In this paper, we propose an accurate 3D road boundary extraction and vectorization method to bridge the gap from unstructured mobile laser scanning (MLS) point clouds to the vector-based representation of road boundary. Firstly, we propose a supervoxel generation method to extract candidate curbs with fine border preservation and high computation efficiencies. Then the candidate curb supervoxels are recognized and clustered to produce continuous road boundary segments with a contracted distance clustering strategy. Finally, the vectorized road boundary is represented by fitting, tracking, and completion from the extracted road boundary segments, resulting in road geometric parameters including boundary location, road widths, turning radius, and slopes. The performance of the proposed method was evaluated on two large-scale datasets collected in urban and industrial areas. Comprehensive experiments reveal that the proposed method is robust to various road shapes and point densities, in terms of precision of 95.0% and recall of 91.0%, respectively.

ROAD EXTRACTION AND VECTORIZATION FROM AERIAL IMAGE DATA

Abstract. The objective of this study is the automatic extraction of the road network in a scene of the urban area from high resolution aerial image data. Our approach includes two stages aiming to solve two important issues respectively, i.e., an effective road extraction pipeline, and a precise vectorized road map. In the first stage, we proposed a so-called all element road model which describes a multiple-level structure of the basic road elements, i.e. intersection, central line, side lines, and road plane based on their spatial relations. An advanced road network extraction scheme was proposed to address the issues of tedious steps on segmentation, recognition and grouping, using the digital surface model (DSM) only. The key feature of the proposed approach was the cross validation of the road basic elements, which was applied all the way through the entire procedure of road extraction. In the second stage, the regularized road map was produced where center line and side lines subject to parallel and even layout rules. It gives more accurate and reliable map by utilizing both the height information of the DSM and the color information of the ortho image. Road surface was extracted from the image by region growing. Then, a regularized center line was modeled by linear regression using all the pixels of the road surface. The road width was estimated and two road side lines were modeled as the straight lines parallel with the center line. Finally, the road model was built up in terms of vectorized points and lines. The experimental results showed that the proposed approach performed satisfactorily in our test site.

Large-scale urban functional zone mapping by integrating remote sensing images and open social data

ABSTRACT Urban functional zones (UFZs) are important for urban sustainability and urban planning and management, but UFZ maps are rarely available and up-to-date in developing countries due to frequent economic and human activities and rapid changes in UFZs. Current methods have focused on mapping UFZs in a small area with either remote sensing images or open social data, but large-scale UFZ mapping integrating these two types of data is still not be applied. In this study, a novel approach to mapping large-scale UFZs by integrating remote sensing images (RSIs) and open social data is proposed. First, a context-enabled image segmentation method is improved to generate UFZ units by incorporating road vectors. Second, the segmented UFZs are classified by coupling Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM). In the classification framework, physical features from RSIs and social attributes from POI (Point of Interest) data are integrated. A case study of Beijing was performed to evaluate the proposed method, and an overall accuracy of 85.9% was achieved. The experimental results demonstrate that the presented method can provide fine-grained UFZs, and the fusion strategy of RSIs and POI data can distinguish urban functions accurately. The proposed method appears to be promising and practical for large-scale UFZ mapping.

An Ensemble Wasserstein Generative Adversarial Network Method for Road Extraction From High Resolution Remote Sensing Images in Rural Areas

Road extraction from high resolution remote sensing (HR-RS) images is an important yet challenging computer vision task. In this study, we propose an ensemble Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) method called E-WGAN-GP for road extraction from HR-RS images in rural areas. The WGAN-GP model modifies the standard GANs with Wasserstein distance and gradient penalty. We add a spatial penalty term in the loss function of the WGAN-GP model to solve the class imbalance problem typically in road extraction. Parameter experiments are undertaken to determine the best spatial penalty and the weight term in the new loss function based on GaoFen-2 dataset. In addition, we execute an ensemble strategy in which we first train two WGAN-GP models using the U-Net and BiSeNet as generator respectively, and then intersect their inferred outputs to yield better road vectors. We train our new model with GaoFen-2 HR-RS images in rural areas from China and also the DeepGlobe Road Extraction dataset. Compared with the U-Net, BiSeNet, D-LinkNet and WGAN-GP methods without ensemble, our new method makes a good trade-off between precision and recall with F1-score = 0.85 and IoU = 0.73.

Road Extraction Based on Level Set Approach From Very High-Resolution Images With Volunteered Geographic Information

The road network is of great importance to the modern traffic system, which changes frequently as its high speed of development. Simultaneously, a growing number of satellites provide a considerable number of remote sensing images that can be used for road extraction. However, most existing methods cannot get accurate results in the high-resolution remote sensing, since the complex background like the shadows and occlusions. Consequently, we proposed a method utilizing the volunteered geographic information data to extract the road from satellite images. At first, from Mapbox platform, the Volunteered Geographic Information data, OpenStreetMap vector tiles are collected to guide the downloading of corresponding road satellite images. Then a level set method is used to extract the road areas from the satellite images supported by the road vector. After that, a superpixel method is applied to improve the road areas by fusing the superpixels with the previous results. Experiments are performed on the satellite images from Mapbox, Google, and Yahoo in the Tianjin Port district on accuracy and efficiency. Experimental results demonstrate that our approach is more accurate and of higher performance than the other two state-of-the-art methods.

Topology-Preserving and Geometric Feature-Correction Watermarking of Vector Maps

Digital watermarking is an effective method of vector map copyright protection. However, the topology and geometric features are potentially influenced by the disturbances of vertices caused by watermark embedding, thus reducing the availability of watermarked vector maps. In this research, we propose a post-correction method for two-dimensional vector map watermarking, by which the topology and geometric features of the input data can be preserved, while using only conventional watermarking techniques. In the proposed method, the Maximum Perturbation Regions (MPR) of vertices and direction angle constraints methods of adjacent lines are adopted to undergo multi-azimuth checks of watermarked vertices, which may cause changes of the geometric features and topology. After that, the coordinate adjustment method which is based on the homonymous vertices topology association and the MPR are combined for topology and geometric feature correction. For watermark embedding, two classic frequency domain watermarking techniques, Discrete Fourier Transformation (DFT) and Discrete Wavelet Transformation (DWT), phase based and low-frequency coefficient based respectively, are also adopted. The scheme was conducted on a building vector map and a road vector map. The experimental results show that the proposed method can ensure the topology consistency and geometric feature similarity of vector maps before and after embedding watermarks. Moreover, this method has little interference with the maps, which improves the usability of the watermarked vector map.

Maintaining accurate, current, rural road network data: An extraction and updating routine using RapidEye, participatory GIS and deep learning

Accurate and current road network data is fundamental to land management and emergency response, yet challenging to produce for unpaved roads in rural and forested regions using traditional cartographic approaches. Automatic extraction of roads from satellite imagery using deep learning is a promising alternative gaining increasing attention, however most efforts have focused on urban paved roads and used very high spatial resolution imagery, which is less frequently available for rural regions. Additionally, road extraction routines still struggle to produce a fully-connected, vectorized road network. In this study covering a large forested area in Western Canada, we developed and evaluated a routine to automatically extract unpaved road pixels using a convolutional neural network (CNN), and then used the CNN outputs to update a pre-existing government road network and evaluate if and how it would change. To cover the large spatial extent mapped in this study, we trained the routine using moderately high-resolution satellite imagery from the RapidEye constellation and a ground-truth dataset collected with smartphones by organizations already operating and driving in the region. Performance of the road extraction was comparable to results achieved by others using very high-resolution imagery; recall accuracy was 89–97%, and precision was 85–91%. Using our approach to update the pre-existing road network would result in both removals and additions to the network, totalling over 1250 km, or about 20 % of the roads previously in the network. We discuss how road density estimates in the study area would change using this updated network, and situate these changes within the context of ongoing efforts to conserve grizzly bears, which are listed as a Threatened species in the region. This study demonstrates the potential of remote sensing to maintain current and accurate rural road networks in dynamic forest landscapes where new road construction is prevalent, yet roads are also frequently de-activated, reclaimed or otherwise not maintained.

The Evolution and Growth Patterns of the Road Network in a Medium-Sized Developing City: A Historical Investigation of Changchun, China, from 1912 to 2017

Understanding the evolution and growth patterns of urban road networks helps to design an efficient and sustainable transport network. The paper proposed a general study framework and analytical workflow based on network theory that could be applied to almost any city to analyze the temporal evolution of road networks. The main tasks follow three steps: vector road network drawing, topology graph generation, and measure classification. Considering data availability and the limitations of existing studies, we took Changchun, China, a middle-sized developing city that is seldom reported in existing studies, as the study area. The research results of Changchun (1912–2017) show the road networks sprawled and densified over time, and the evolution patterns depend on the historical periods and urban planning modes. The evolution of network scales exhibits significant correlation; the population in the city is well correlated with the total road length and car ownership. Each network index also presents specific rules. All road networks are small-world networks, and the arterial roads have been consistent over time; however, the core area changes within the adjacent range but is generally far from the old city. More importantly, we found the correlation between structure and function of the urban road networks in terms of the temporal evolution. However, the temporal evolution pattern shows the correlation varies over time or planning modes, which had not been reported

Extracting Flooded Roads by Fusing GPS Trajectories and Road Network

Urban roads are the lifeline of urban transportation and satisfy the commuting and travel needs of citizens. Following the acceleration of urbanization and the frequent extreme weather in recent years, urban waterlogging is occurring more than usual in summer and has negative effects on the urban traffic networks. Extracting flooded roads is a critical procedure for improving the resistance ability of roads after urban waterlogging occurs. This paper proposes a flooded road extraction method to extract the flooding degree and the time at which roads become flooded in large urban areas by using global positioning system (GPS) trajectory points with driving status information and the high position accuracy of vector road data with semantic information. This method uses partition statistics to create density grids (grid layer) and uses map matching to construct a time-series of GPS trajectory point density for each road (vector layer). Finally, the fusion of grids and vector layers obtains a more accurate result. The experiment uses a dataset of GPS trajectory points and vector road data in the Wuchang district, which proves that the extraction result has a high similarity with respect to the flooded roads reported in the news. Additionally, extracted flooded roads that were not reported in the news were also found. Compared with the traditional methods for extracting flooded roads and areas, such as rainfall simulation and SAR image-based classification in urban areas, the proposed method discovers hidden flooding information from geospatial big data, uploaded at no cost by urban taxis and remaining stable for a long period of time.

Registration method for GIS vector road data and SAR image

Road detection of synthetic aperture radar (SAR) image is an important part of SAR image interpretation. Thus, in the influence of sophisticated background and speckle noise, it is hard to directly extract the road information from the SAR image. So a SAR image registration method using existing geographic information system (GIS) road data is proposed to assist road detection for SAR image in this paper. By making full use of road geometry structure information, a road structure support is defined. Then all the positions in the SAR image are searched to find the biggest structure support as the optimum matching centre. The image registration for SAR image and road vector data is finally realised. Experimental results show that the registration accuracy of the proposed method is ∼2 pixels, which can be used for shoreline extraction, target positioning and so on.

Recommendation of feeder bus routes using neural network embedding-based optimization

Despite the existence of vast bus and subway networks, the demand for taxis in the morning commute hours is substantial in metropolitan areas. This kind of morning traffic can be resolved by means of feeder buses connecting residential areas with popular transit points. To assist the design of feeder bus routes, this study proposes an optimization approach based on road2vec, which is applied to real-time taxi GPS data. Road2vec is a neural network-based embedding methodology that extracts road name vectors considering the movement patterns of vehicles. Subsequently, the k-means clustering analysis is applied to those vectors to identify the major taxi transit clusters during the commute hours. For each cluster, we suggest a feeder bus route that can best reflect the taxi trajectory patterns. To find intermediate stops between the departure and origin of a feeder bus route, we solve an integer programming to maximize the cosine similarity between the origin road vector and the departure road vector subtracted by the road vectors of intermediate stops. The suggested routes based on our method differ from the existing routes in that they have a tendency to pass through residential areas, transit stations, and schools. In addition, the result suggests that the model developed in this study finds bus routes that could be suitable for feeder buses by accommodating areas where the demand for taxis is high in the morning. Our road2vec approach is expected to contribute to a reduction in traffic during rush hours.

Road-Network-Based Fast Geolocalization

It has always been a research hotspot to use geographic information to assist the navigation of unmanned aerial vehicles. In this paper, a road-network-based localization method is proposed. We match roads in the measurement images to the reference road vector map, and realize successful localization on areas as large as a whole city. The road network matching problem is treated as a point cloud registration problem under two-dimensional projective transformation, and solved under a hypothesise-and-test framework. To deal with the projective point cloud registration problem, a global projective invariant feature is proposed, which consists of two road intersections augmented with the information of their tangents. We call it two road intersections tuple. We deduce the closed-form solution for determining the alignment transformation from a pair of matching two road intersections tuples. In addition, we propose the necessary conditions for the tuples to match. This can reduce the candidate matching tuples, thus accelerating the search to a great extent. We test all the candidate matching tuples under a hypothesise-and-test framework to search for the best match. The experiments show that our method can localize the target area over an area of 400 within 1 second on a single cpu.

Spatial distribution and accessibility to public sector tertiary care teaching hospitals: case study from Pakistan.

Public sector provision of health care services is important for addressing health morbidity and mortality burden for population health in developing countries like Pakistan. Physical accessibility and spatial distribution of healthcare facilities affect their utilization for improving population health. This study aimed to analyze the spatial distribution of five public sector, tertiary care, teaching, general hospitals in the city of Lahore, Pakistan. The point locations of all five hospitals were obtained from Google Earth. While the vector road network file was downloaded from the OpenStreetMap website. GIS software ArcGIS 10.5 and the Network Analyst extension were used for processing, analysis and mapping. The Lahore city shapefiles and the hospital point locations were projected to ensure correct spatial distance analysis. The five hospitals are located in the northeastern and east-central parts of Lahore. Most of the Lahore "towns" (administrative subdivisions) are either partially or completely covered by the 12-kilometre buffer as well as the 12-kilometre service area around these five selected hospitals. However, either the 12-kilometre buffer or service area did not cover large swaths of Wagha, Nishtar, and Iqbal towns. Availability of geographic and attribute data on road network and population characteristics would facilitate planning for allocating locations for new health care facilities based on utilitarian principles.

An automatic method for road centerline extraction from post-earthquake aerial images

Road vector database plays an important role in post-earthquake relief, rescue and reconstruction. However, due to data privacy policy, it is difficult for general users to obtain high-precision and complete vector data of road network. The OpenStreetMap (OSM) project provides an open-source, global free road dataset, but there are inevitable geo-localization/projection errors, which will lead to large errors in hazard survey analysis. In this paper, we proposed a road centerline correction method using post-earthquake aerial images. Under the constraint of the vector road map (OpenStreetMap), we rectified the centerline by the context feature and spectral gradient feature of post-event images automatically. The experiment implemented on 0.5 m/pixel post-event aerial images of Haiti, 2010, showed that the completeness and extraction quality of proposed method were over 90% and 80% without any manual intervention.

Automatic Registration Between Low-Altitude LiDAR Point Clouds and Aerial Images Using Road Features

Among the many means of acquiring surface information, low-altitude light detection and ranging (LiDAR) systems (e.g., unmanned aerial vehicle LiDAR, UAV-LiDAR) have become an important approach to accessing geospatial information. Considering the lower level of hardware technology in low-altitude LiDAR systems compared to that in airborne LiDAR, and the greater flexibility in-flight, registration procedures must be first performed to facilitate the fusion of laser point data and aerial images. The corner points and edges of buildings are frequently used for the automatic registration of aerial imagery with LiDAR data. Although aerial images and LiDAR data provide powerful support for building detection, adaptive edge detection for all types of building shapes is difficult. To deal with the weakness of building edge detection and reduce matching-related computation, the study presents a novel automatic registration method for aerial images, with LiDAR data, on the basis of main-road information in urban areas. Firstly, vector road centerlines are extracted from raw LiDAR data and then projected onto related aerial images with the use of coarse exterior orientation parameters (EOPs). Secondly, the corresponding image road features of each LiDAR vector road are determined using an improved total rectangle-matching approach. Finally, the endpoints of the conjugate road features obtained from the LiDAR data and aerial images are used as ground control points in space resection adjustment to refine the EOPs; an iterative strategy is used to obtain optimal matching results. Experimental results using road features verify the feasibility, robustness and accuracy of the proposed approach.

Road Extraction using Deep Learning

The Road extraction from aerial image, stands as a quintessential node for the development of rudimentary layers in innumerable fields. From GIS, to Unmanned Aerial vehicles, road maps pave the foundation for data accumulation. This significant process is a result of number of mechanisms devised over the years through iterative experiments and research. However, the glut of methods available often pose as a hurdle in the selection process. In this project we implement a novel approach to solve the extraction problem, by incorporating generative algorithm using conditional adversarial networks. We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. The U-Network incorporated essentially convolves and de-convolves over the generative model, thus producing a pixel to pixel image translation, the result of which is the vector road map of its corresponding aerial image. The entire model is trained on a 990 MS GPU for computational ease.

The spatial suitability evaluation of networked minimarket locations according to Bupati Bantul Regulation No. 35/2013

The Regulation of Bupati Bantul Number 35/2013 regulates the establishment of networked minimarket (mini market berjejaring) in Bantul Regency. This study was conducted to assess the suitability between the regulation with the real condition by emphasizing the spatial aspects after 5 years of the implementation of the regulation. This study concerns with the criteria in the regulation about the distance of the mini markets from the traditional markets and the class of the road. The data for cunducting the spatial analysis include road vector data, administrative boundaries, locations of traditional market and networked minimarket, attribute data for networked minimarket, and satellite images of Bantul Regency. The data then was analyzed using proximity and network analysis to know suitability of networked minimarket locations with the regulation. Proximity analysis was done using buffer operation, meanwhile network analysis was done using operation of OD Cost Matrix. Result obtained from the proximity analysis of the overall parameters show that there is only one minimarket in accordance with the regulation. In the other hand, , if the network analysis is included, then the number of minimarkets that comply with the regulation increase to three minimarkets. To conclude, most of the locations of the netwoked minimarkets in Bantul do not comply with the Bupati Bantul Regulation Number 35/2013.

Adaptive Obstacle Detection for Mobile Robots in Urban Environments Using Downward-Looking 2D LiDAR.

Environment perception is important for collision-free motion planning of outdoor mobile robots. This paper presents an adaptive obstacle detection method for outdoor mobile robots using a single downward-looking LiDAR sensor. The method begins by extracting line segments from the raw sensor data, and then estimates the height and the vector of the scanned road surface at each moment. Subsequently, the segments are divided into either road ground or obstacles based on the average height of each line segment and the deviation between the line segment and the road vector estimated from the previous measurements. A series of experiments have been conducted in several scenarios, including normal scenes and complex scenes. The experimental results show that the proposed approach can accurately detect obstacles on roads and could effectively deal with the different heights of obstacles in urban road environments.

Reliable Subpixel Ground Control Point Estimation Algorithm Using Vector Roads

This letter proposes a novel algorithm for reliable and accurate ground control point (GCP) estimation, which uses vector road network data as a reference. Roads are detected in satellite images, and road patches are used as a template, which is matched against the vector road data by using a nonlinear criterion based on the distance transform. The proposed approach is compared with the standard correlation-based template matching in terms of the reliability and accuracy of localization on a test set of five RapidEye images with 380 reference control points. The experiments demonstrate superior reliability with respect to the size of the search window and the variable image acquisition conditions. While the subpixel accuracy is achieved by the cross-correlation method over the search area up to 20% of the template size, the proposed method achieves comparable accuracy over a search area that is ten times larger than the template image.