Complex Road Intersection Modelling Based on Low-Frequency GPS
Track Data

Huang, Jincai; Deng, Min; Zhang, Y.; Liu, H.

doi:10.5194/isprs-archives-xlii-2-w7-23-2017

Cited by 4 publications

(9 citation statements)

References 21 publications

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“…• Line segment matching. Within the search buffer, a line segment set with n elements is established based on Equation (2). As shown in Equation (3), any line segment acquired in the buffer zone is selected as a line segment to be matched and judged by comparing it with all road directions (by Angle) constituting the intersection.…”

Section: B Geometry and Texture Of Intersections 1) Geometric Analysismentioning

confidence: 99%

“…Road intersections play a bridging role in road networks, which can provide information on road connectivity and turn availability [1]. Automatic extraction of road intersection from remote sensing images is an important prerequisite for applications such as extraction and updating of road networks [2], [3], collecting road sample collections from satellite images for deep learning models [4], [5], dynamic monitoring of traffic control [6]. With the deep application of remote sensing technology, road intersection points are also used in data registration between vector data and remote sensing images [7], [8], aircraft-assisted navigation [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…Habermann et al [23] proposed a ''featureclassification-structure modeling'' algorithm to detect road intersections from vehicle point cloud data. Huang et al [2] computed road intersection structures by k-segment principle curve fitting through spatially clustering nearby low-frequency vehicle GPS trajectory data using longest common subsequence and directions of trajectories. This kind of methods is prone to deviation.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Method for Extraction of Complex Road Intersection Points From High-Resolution Remote Sensing Images Based on Fuzzy Inference

Dai

Wang

et al. 2020

IEEE Access

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Automatic extracting road intersection points is essential for applications such as data registration between vector data and remote sensing images, aircraft-assisted navigation. However, at a large scale, it is difficult to quickly and accurately extract road intersection points due to the problems caused by complex structures, geometric texture noise interference. In this context, taking OpenStreetMap (OSM) data as priori knowledge, we propose a method for automatic extraction of complex road intersection points based on fuzzy inference. First, OSM data are analyzed to obtain structural information of intersection points. Local search areas are built around the intersection points. Second, within the local search area, the candidate intersection point set are generated. Meanwhile the input image is segmented using multiresolution segmentation; then we establish a fuzzy rule to infer the road area from the segmentation result. The fuzzy indexes and rules are established for the candidate intersection point set to deduce the road intersection area. Finally, based on the results of the previous step, the road intersection points are extracted based on the line segment constraint, structure matching, and linkage equation. Three sets of high-resolution remote sensing images were used to verify the feasibility of the method. We demonstrate that the correctness and positioning accuracy of this method are superior to those of other methods through contrastive analysis.

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Section: B Geometry and Texture Of Intersections 1) Geometric Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Method for Extraction of Complex Road Intersection Points From High-Resolution Remote Sensing Images Based on Fuzzy Inference

Dai

Wang

et al. 2020

IEEE Access

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“…For example, Huang et al attempt to construct precise models for complex road intersections by using low frequency GPS traces. They first clustered the vehicle trajectories and then determined the geometry shapes of complex road intersections by a K-segment principle curve algorithm [5] . Other scholars obtained existing intersection types from geospatial data and road intersection features, and extracted road intersections from vector data based on these features [6] .…”

Section: Introductionmentioning

confidence: 99%

Road intersection detection based on YOLOv5s and topological analysis: a case study of Shijiazhuang

Chang,

Wang,

Geng

et al. 2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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As urbanization and the rapid development of remote sensing technology, the interpretation of urban roads and intersections using high-resolution remote sensing images has become the focus of studying urban planning and road network structure. However, the current research is only limited to extracting road intersections, and few studies have constructed their topological relationships and analyzed the spatial distribution of road networks. Therefore, in this study, the intersection targets are extracted based on YOLOv5s model, and then the topological relationship of intersections is constructed by Delaunay Triangulation. The accuracy of the optimal model is 0.9415 for mAP, 0.8985 for F1, 0.8611 for Recall, and 0.9394 for Precision. By analyzing their characteristics such as area, side length, PESL, AESL, ESLR and interior angle, it can be found that the density of road intersections in the study area is high, the spacing of adjacent intersections of most roads is relatively similar, and most of the roads intersect vertically. This study first extracts road intersections from remote sensing images and then analyzes their topological relationships to provide research ideas for large-scale urban road network analysis and multi-source road data matching.

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“…With the development of surveying, mapping, communications, computers, and other technologies, we can infer road networks based on various data sources, such as crowd-sourced vehicle trajectories [ 2 , 3 , 4 , 5 ], laser point clouds [ 6 , 7 ], remote sensing images [ 8 , 9 ], aerial images [ 10 , 11 , 12 ], OpenStreetMap [ 13 , 14 , 15 ], etc. Among these data sources, crowd-sourced trajectories have become mainstream data sources of generating road information, and have triggered a large amount of research on road extraction in the past few years, focusing on prominent features, such as wide coverage, high update frequency, and low acquisition cost [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Generating Road Networks for Old Downtown Areas Based on Crowd-Sourced Vehicle Trajectories

Cai-li

Xiang

et al. 2021

Sensors

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With the popularity of portable positioning devices, crowd-sourced trajectory data have attracted widespread attention, and led to many research breakthroughs in the field of road network extraction. However, it is still a challenging task to detect the road networks of old downtown areas with complex network layouts from high noise, low frequency, and uneven distribution trajectories. Therefore, this paper focuses on the old downtown area and provides a novel intersection-first approach to generate road networks based on low quality, crowd-sourced vehicle trajectories. For intersection detection, virtual representative points with distance constraints are detected, and the clustering by fast search and find of density peaks (CFDP) algorithm is introduced to overcome low frequency features of trajectories, and improve the positioning accuracy of intersections. For link extraction, an identification strategy based on the Delaunay triangulation network is developed to quickly filter out false links between large-scale intersections. In order to alleviate the curse of sparse and uneven data distribution, an adaptive link-fitting scheme, considering feature differences, is further designed to derive link centerlines. The experiment results show that the method proposed in this paper preforms remarkably better in both intersection detection and road network generation for old downtown areas.

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Complex Road Intersection Modelling Based on Low-Frequency GPS Track Data

Cited by 4 publications

References 21 publications

Automatic Method for Extraction of Complex Road Intersection Points From High-Resolution Remote Sensing Images Based on Fuzzy Inference

Automatic Method for Extraction of Complex Road Intersection Points From High-Resolution Remote Sensing Images Based on Fuzzy Inference

Road intersection detection based on YOLOv5s and topological analysis: a case study of Shijiazhuang

Generating Road Networks for Old Downtown Areas Based on Crowd-Sourced Vehicle Trajectories

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