Automatic road network selection method considering functional semantic features of roads with graph convolutional networks

Tang, Jianbo; Deng, Min; Peng, Ju; Liu, Huimin; Yang, Xuexi; Chen, Xueying

doi:10.1080/13658816.2024.2387198

International Journal of Geographical Information Science

2024

DOI: 10.1080/13658816.2024.2387198

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Automatic road network selection method considering functional semantic features of roads with graph convolutional networks

Jianbo Tang,

Min Deng,

Ju Peng

et al.

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2024

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Selection of Grid Road Networks Based on Raster Data

Shen,

Zhang,

2024

Applied Sciences

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In cartography, generalization is a key process used to simplify complex geographic information, making it suitable for display at different scales while maintaining its essential meaning. When representing high-density road networks on a fixed screen area, overcrowding and loss of clarity often occur. To solve these problems, a road selection operation can be applied. However, traditional methods have primarily focused on structured vector road networks, leaving unstructured raster road networks largely unaddressed. This study introduces a novel technique, Adaptive Road Width Selection (ARWS), designed to improve the multiscale visualization of compact road systems using unstructured raster datasets. The ARWS method begins by segmenting the original raster road network into multilevel superpixels of varying sizes, reflecting the road widths, through neighborhood analysis. Next, road superpixel matching and selection are performed based on the minimum angle and maximum distance rules, alongside shortest-path calculations. Finally, redundant intersection pixels are eliminated to generate the selection results. The proposed ARWS method was evaluated using road network data from Shenzhen, China, producing effective multiscale visualization outcomes. Unlike conventional techniques relying on structured vector data, ARWS excels in preserving the semantic attributes, overall structure, local connectivity, and integrity of road networks. It addresses the challenges of multiscale visualization in dense road networks, offering a robust solution for unstructured raster data.

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Selection of Grid Road Networks Based on Raster Data

Shen,

Zhang,

2024

Applied Sciences

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Automatic road network selection method considering functional semantic features of roads with graph convolutional networks

Cited by 1 publication

References 45 publications

Selection of Grid Road Networks Based on Raster Data

Selection of Grid Road Networks Based on Raster Data

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