Intersection Detection Based on Qualitative Spatial Reasoning on Stopping Point Clusters

Zourlidou, Stefania; Sester, Monika

doi:10.5194/isprs-archives-xli-b2-269-2016

Cited by 8 publications

(5 citation statements)

References 29 publications

(10 reference statements)

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“…Grid-based methods rasterize the scatter trajectory points and apply mathematical morphology [37] and topical model-based methods [38] to extract the road centerlines. Intersection-connection methods extract road intersections by calculating trajectory sampling point characteristics [39], such as density, direction, and speed, or by analyzing the geometric features of the road [40], and then generate road links to connect the intersections. However, these methods still cannot eliminate noise or extract roads in regions with few or even zero tracking points, such as low-level roads.…”

Section: Related Workmentioning

confidence: 99%

A Guided Deep Learning Approach for Joint Road Extraction and Intersection Detection From RS Images and Taxi Trajectories

Xiang

Cai-li

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Automatic extraction of road information based on data-driven methods is significant for various practical applications. Remote sensing (RS) images and GPS trajectories are two available data sources that can describe roads from a complementary perspective, and fusing them together can improve road detection performance. However, existing studies on the combination of RS images and GPS trajectories do not fully utilize their enhanced information about roads and suffer from road information loss. Moreover, roads and intersections are two crucial elements of road network generation, and they are closely related to each other. Therefore, we propose a multi-task and multi-source adaptive fusion (MTMSAF) network, which leverages RS images and trajectory datasets to execute road extraction tasks and intersection detection tasks simultaneously. The MTMSAF network is built on three components. First, two encoder steams are designed to capture road features from RS images and trajectories. Then, an adaptive fusion module is created to fuse the individual road features at each level in a guiding fashion. Finally, two specific decoders are proposed: one is used to implement the road region detection task by considering multidirectional geometric road information, and the other is used to perform the intersection extraction task by recovering intersection information with the road region feature. Four stateof-the-art deep learning-based methods, including segmentation networks and road extraction networks, are compared with the proposed approach. The results show the superiority of our approach for both road detection and intersection extraction tasks.

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Section: Related Workmentioning

confidence: 99%

A Guided Deep Learning Approach for Joint Road Extraction and Intersection Detection From RS Images and Taxi Trajectories

Xiang

Cai-li

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Intersection-link methods detect road intersections first based on density distribution of trajectory sampling points and their implicit semantic features [ 15 , 38 ], trajectory point direction, speed, and their implicit dynamic features [ 17 , 39 ], and then connect these intersections to form the road network. However, current research mainly focuses on intersection extraction, and seldom conduct further road network generation [ 40 ].…”

Section: Related Workmentioning

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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“…They modeled the problem as a regression problem and proposed a general framework based on PageRank to assign weights to the edges that are not covered by the input dataset [15]. Zourlidou and Sester proposed an approach that detects the road intersections by extracting semantic features of the trajectories in form of sequences of stops and moves [24]. Their approach can detect the intersections even when the samples are not available from all road segments that participate in the intersections.…”

Section: Related Workmentioning

confidence: 99%

Building road segments and detecting turns from GPS tracks

Ezzat

Sakr

Elgohary

et al. 2018

Journal of Computational Science

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With the wide spreading of geo-aware mobile applications, huge amounts of user-contributed GPS trajectories become available with different levels of accuracy. Constructing road maps from such datasets is one important benefit from this data, and it is required for many applications. There are some challenges related to the inaccuracies incurred on real datasets, such as missing GPS signals, low sampling rate and bad driving behaviour. In this paper, we present a new preprocessing algorithm to address the problems of GPS data. Additionally, we present a clustering-based technique to extract the road map from GPS tracks. Firstly, the tracks are simplified in order to extract road turns and remove the noise data. Then, we adjust the points of the simplified tracks to solve the problems caused by the low sampling rate by moving them closer to the positions of the real turns. Afterwards, a progressive clustering is applied to extract turns and intersections. Finally, we connect them to build the road segments. To ensure the accuracy of our results, we compare the proposed technique with two of the best state-of-the-art methods using a small-scale dataset with inconsistent sampling rate. Another experiment is conducted by extracting a part of the road segments of Egypt using a large-scale dataset with more than 12 million GPS points that are captured with high sampling rate. Experimental results show that our proposed technique exceeds the other methods with regard to F-measure.

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Intersection Detection Based on Qualitative Spatial Reasoning on Stopping Point Clusters

Cited by 8 publications

References 29 publications

A Guided Deep Learning Approach for Joint Road Extraction and Intersection Detection From RS Images and Taxi Trajectories

A Guided Deep Learning Approach for Joint Road Extraction and Intersection Detection From RS Images and Taxi Trajectories

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

Building road segments and detecting turns from GPS tracks

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