ROAD NETWORK EXTRACTION USING GPS TRAJECTORIES BASED ON MORPHOLOGICAL AND SKELETONIZATION ALGORITHMS

Author:

Dal Poz A. P.,Martins E. F. O.,Zanin R. B.

Abstract

Abstract. In this article, a method for road network extraction is proposed, based on GPS (Global Positioning System) trajectories. Unlike existing methods, it is not necessary to resample the GPS trajectories into a raster structure; instead, all analyses are based on the polylines that represent the GPS trajectories. Basically, a morphological analysis and a skeletonization technique are used by the proposed method. Two main steps of the method can be identified: the first step consists in generating an elongated polygon (that delimitates an elongated ribbon) that represents the selected road; and the second step aims at reconstructing the road network. The proposed method was evaluated based on four GPS trajectory datasets and the results obtained can be considered good, but some inconsistencies were noted, as for example: extraction failures occur in places with very low trajectory density (such as 3–4 trajectories); merging of very close and parallel roads; some road crossings that are close to one another have been merged into a single point. The proposed method was also compared with existing methods in the literature and the obtained results showed good consistency between them.

Publisher

Copernicus GmbH

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Urban and Rural Road Extraction From Smartphone-Based GPS Trajectories;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

2. Residual Channel Attention Fusion Network for Road Extraction Based on Remote Sensing Images and GPS Trajectories;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

3. Road extraction from low-cost GNSS-device dense trajectories;Journal of Location Based Services;2023-05-27

4. Road Topology Extraction From Satellite Imagery by Joint Learning of Nodes and Their Connectivity;IEEE Transactions on Geoscience and Remote Sensing;2023

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