Road Network Extraction from GPS Trajectories – A Tensor Voting

Based Algorithm

Yan Luo*1, Longgang Xiang†2, Yang Xu‡1and Zhipeng Gui§3 1Department of Land Surveying and Geo-informatics, the Hong Kong Polytechnic University

2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University

3School of Remote Sensing and Information Engineering, Wuhan University

Jan 14, 2020

Summary This paper introduces a tensor voting based algorithm for automatic road extraction from GPS trajectories. By performing the algorithm over three selected sites in Wuhan, China, the experimental results show that the proposed method can extract comprehensive road networks by effectively identifying intersections and road sections. The algorithm has a good anti-noise effect and remains robust across the three experimental sites.

KEYWORDS: Tensor Voting; Road Network Extraction; GPS Trajectories; Machine Learning 1. Background Road network serves as a key component of many digital maps (Harvey, McGlone, McKeown, & Irvine, 2004). Extracting accurate and up-to-date information of road networks is particularly important for navigation, transport planning, and many other urban applications. Traditional ways of road detection mainly include field survey based on vehicle-borne 3D data acquiring system and remote sensing image detection (Géraud & Mouret, 2004; Hu, Razdan, Femiani, Cui, & Wonka, 2007; Lisini, Tison, Tupin, & Gamba, 2006; Song & Civco, 2004; Tupin, Maitre, Mangin, Nicolas, & Pechersky, 1998; Wegner, Montoya-Zegarra, & Schindler, 2013). The former approach usually achieves high accuracy, but is costly and time-consuming; the latter can automatically generate road network based on some existing algorithms. However, due to the problems of remote sensing image distortion, ground object occlusion, uneven image quality and long acquisition period, the accuracy and immediacy are relatively low (Fathi & Krumm, 2010; Huang, Zhu, Li, Li, & Wu, 2010) (Karagiorgou & Pfoser, 2012; W. Shi, Shen, & Liu, 2009; X. Shi, Ling, Blasch, & Hu, 2012). Recent advancements of location-aware and information technologies have produced a variety of big datasets, providing new opportunities for automatic road detection at urban scale. Road detection and extraction is a key research topic for both industry and academia. Different data sources and different algorithms often generate different results. The purpose of this study is to use the current big data technology and tensor voting – a machine-learning method – to detect road information. In doing so, we can obtain more accurate and effective prediction results, which can provide effective help for residents' travel and transportation department’s decisions.

* silver.luo@connect.polyu.hk † geoxlg@whu.edu.cn ‡ yang.ls.xu@polyu.edu.hk § zhipeng.gui@whu.edu.cn

2. Tensor Voting Algorithm The tensor voting algorithm was first proposed in the application of computer vision. But in this study, we will explore whether it can be used on road network extraction. The algorithm is based on Gestalt Theory. The theory advocates that human vision has a tendency to integrate and simplify the processing of graphics. Therefore, when an incomplete graphic appears in human vision, human visual thinking will tend to automatically complete it and make it into a known, complete, common, overall figure, that is, ‘Gestalt’. For example, when you see a circle, but there is a small gap on the edge of the circle, your brain will tend to recognize it as a complete circle, that is, the end of the gap will have a tendency to approach and bridge each other. But when this notched circle is placed in many complete circles, your brain will quickly recognize it, because the brain can quickly process the complete circles and ignore them, but the incomplete round shape will attract the attention of the brain, treat it specially, and complete it. According to Gestalt psychology, points and lines are powerful fields. The basic principles therein will affect the composition. The tensor voting algorithm is based on Gestalt Theory. It argues the unknown structure of a token can be known by voting by other tokens in a picture. The core of tensor voting can be summarized as tensor representation and information propagation mechanism. Tokens can be divided into undirected tokens and directed tokens. Undirected tokens indicate the possibility that there is an unknown type of perception structure at the location of tokens. Directed tokens can represent basic curve elements, surface elements, etc. By voting, we can combine the two possibilities to speculate the saliency of each token to get a significant structure. The token was originally represented by a symmetric second-order tensor. The tensor essentially represents the significance of each perception structure to which the token belongs, as well as its preferred normal or tangent direction. As for the discontinuous structure of perceptual structures (such as unclosed circles), scholars introduced vectors, which are first-order tensors, to solve this problem. The first stage of information propagation is sparse voting, which means voting from one token to another. After this, a large number of first-order and second-order votes are accumulated at each token position. Finally, dense voting needs to be conducted. During this process, the votes for all grid locations are accumulated, inferring the significance of each location. The token-centered tensor fields will be generated. This process allows the continuation of the structure and the joining of gaps. By analyzing the consistency of the direction estimation and the amount of voting support received by the token, we can determine the type of structure present at that location and its significance. The resulting tensors can further be decomposed to obtain the stick tensor saliency and the ball tensor saliency. The ball tensor represents a perception structure without direction preference, or a token position coexisting in multiple directions. The stick tensor represents a perception structure that has a certain direction. 3. Study Area and Dataset This research selects three sites in Wuhan City, China as study cases. They are Wuhan University, Hongshan Square, and Nanhu District, respectively. The spatial extents of the three sites (i.e., bounding boxes of lat/lng) are shown in Table 1. The ground objects in study areas have complex components, including viaducts, main roads, secondary roads, and general urban roads, as well as schools, business areas, hospitals, administrative units, and residential areas. The satellite images of the three sites are shown in Figure 1.

Table 1 Latitude and longitude ranges of study areas

Wuhan University Hongshan Square Nanhu District

[114.360274E~114.383055E]

[30.530513N~30.551788N]

[114.31603E~114.35895E]

[30.52582N~30.56089N]

[114.29706E~114.33101E]

[30.48489N~30.51894N]

(a) Wuhan University (b) Hongshan Square (c) Nanhu District

Figure 1 Satellite images of study areas

The experimental data is the taxi GPS data of Wuhan City in ten days from May 29, 2014, to June 07, 2014. The data has about one million rows. As shown in Table 2, each row represents a GPS record that documents the timestamp, location (lat/lng), the instantaneous speed and the heading of the vehicle (angle between driving direction and north direction).

Table 2 Examples of data

ID time longitude latitude speed heading 10*** 2014-05-30

00:00:27 114.*** 30.*** 12.385 230.580

10*** 2014-05-30 00:01:07

114.*** 30.*** 15.838 229.290

… … … … … … 10*** 2014-05-30

23:58:23 114.*** 30.*** 17.897 134.768

10*** 2014-05-30 23:59:49

114.*** 30.*** 19.377 130.678

After data cleaning, we divide three sites into 200m * 200m grids. Grid attributes are further characterized by the average value of each attribute of each grid. 4. Methods and Results We further expand the flow of the tensor voting algorithm according to the needs of the experiment in this paper. The specific flow chart is shown in Figure 2.

Figure 2 Flow chart of tensor voting algorithm

The initial step of the tensor voting based algorithm is to encode the input tokens. Common features of GPS signal points are point speed, point density, and point direction. Due to the influence of traffic lights, the pause time of GPS signal points on the road intersections is generally longer than that of points on the road sections. Therefore, the GPS signals are collected more at intersections, which results in higher point density; Because of the various driving directions on intersections, the point directions on intersections are more than that of the road section; There may be no significant difference in point speed between road sections and road intersections. We further consider that it is easy to confuse congested sections with intersections if adopting point density. Therefore, this paper uses the attribute of point direction to construct and transform the track points into an adaptive 2-D tensor. After encoding the point direction information of tokens into tensors, we first conduct sparse tensor voting. Then dense tensor voting is performed. In doing so, we can get the resulting tensors, which can be decomposed to obtain the stick tensor saliency and the ball tensor saliency. Because the road intersection has multiple directions, it shows a strong ball tensor saliency. The road section is a line segment, with a strong stick tensor saliency. Therefore, the ball tensor saliency map is corresponding to the road intersection possibility map, and the stick tensor saliency map is corresponding to the road section possibility map (as shown in Figure 3-5).

(a) Raw data (b) Tensor field formed by each token

(c) Saliency map of road sections (d) Saliency map of road intersections

Figure 3 Preliminary results in Wuhan University

(a) Raw data (b) Tensor field formed by each token

(c) Saliency map of road sections (d) Saliency map of road intersections

Figure 4 Preliminary results in Hongshan Square

(a) Raw data (b) Tensor field formed by each token

(c) Saliency map of road sections (d) Saliency map of road intersections

Figure 5 Preliminary results in Nanhu District

After obtaining the preliminary results, we perform post-processing to present the road networks. We apply the seed point growth algorithm on preliminary results so we can get a preliminary map of road sections. There are two main steps in the post-treatment: expansion-corrosion and gap connection. Expansion-corrosion is to thicken and connect the backbone of the road network. And it can remove noise to some extent; the purpose of the gap connection is to bridge the road sections, namely, to fill out the intersections. We detect the endpoints of each part to judge the Euclidean distance to its neighbors. If it is less than a certain threshold, we carry out gap connection. Thus, the intersections are filled out. It can be seen in Figure 6-8 that after the post-processing, the results are ideal since it is consistent with the ground truth. In addition, the noise in the result graphs of expansion-corrosion is a smaller subset of the noise in the raw data, which proves the anti-noise effect of the tensor voting algorithm.

(a) Result of expansion corrosion (b) Result of gap connection

Figure 6 Post-processing results in Wuhan University

(a) Result of expansion corrosion (b) Result of gap connection

Figure 7 Post-processing results in Hongshan Square

(a) Result of expansion corrosion (b) Result of gap connection

Figure 8 Post-processing results in Nanhu District

5. Discussion and Conclusion In this paper, we perform a tensor voting based algorithm to extract road intersections and sections from GPS trajectories. The main workflow of the study are as follows: Firstly, we select experimental areas and clean the data. Then the algorithm combines process of grid transformation and features from point direction for tensor construction. Following that, we conduct tensor voting experiments, to obtain the significant structure of intersections and road sections through sparse voting and dense voting. The significant structures of road sections can then be extracted by using seed point growth algorithm. Finally, the corrosion-expansion and gap connection methods in morphological image processing are used to obtain a complete road network. This paper makes a novel and preliminary attempt on combining tensor voting and GPS trajectories to extract road network. Complete road networks as well as saliency maps of intersections and links are obtained. Besides, the algorithm has proved to have good anti-noise capability. However, due to the complexity of the actual road situation, the variety of ground objects, and the limitations of GPS data, the method proposed in this paper is not perfect. This also points to a few possible future research directions: (1) Tensor voting algorithm cannot avoid traversal. For large areas, the running speed of the algorithm is relatively slow. In the future, we can improve the practicability of the method by reducing the iterations and loops of the algorithm. (2) The result of tensor voting depends on the threshold of several parameters, and the choice of threshold is closely related to data. We can consider repeated experiments for various scenes, and get a better experience threshold, which might further improve the robustness of this method. (3) In the post-processing we use traditional algorithms, the accuracy is not very high, and the operation efficiency is low. In the future, we can consider conducting the gap connection along the road direction, human-computer interaction denoising method, or appropriate denoising in preprocessing. 6. Acknowledgements This work was supported by the Hong Kong Polytechnic University Start-Up Grant (Grant No. 1-BE0J) and the National Science Foundation of China (Grant Nos. 41771474, 41930107). 7. Biography Yan Luo is a PhD student in the Department of Land Surveying and Geo-Informatics (LSGI) at the Hong Kong Polytechnic University. Her research interests include urban computing, spatiotemporal data mining, and graph neural networks. Longgang Xiang is currently a Professor in the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University. His research interests include GPS trajectory data analysis and mining, machine learning of spatio-temporal big data, and NoSQL database management of real-time GIS data. Yang Xu is an Assistant Professor in the Department of Land Surveying and Geo-Informatics (LSGI) at the Hong Kong Polytechnic University. His research interests include geographic information science, transportation, and urban informatics. Zhipeng Gui is an Associate Professor of Geographic Information Science in the School of Remote Sensing and Information Engineering, Wuhan University. His research interests are high-performance spatiotemporal data mining and geovisual analytics.

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