Selection of Grid Road Networks Based on Raster Data
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 occ...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/23/11451 |
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| author | Yilang Shen Yiqing Zhang Renzhu Li |
| author_facet | Yilang Shen Yiqing Zhang Renzhu Li |
| author_sort | Yilang Shen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0cfd0ff2badb475a80787e35212fc1a4 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0cfd0ff2badb475a80787e35212fc1a42024-12-13T16:23:57ZengMDPI AGApplied Sciences2076-34172024-12-0114231145110.3390/app142311451Selection of Grid Road Networks Based on Raster DataYilang Shen0Yiqing Zhang1Renzhu Li2School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, ChinaIn 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.https://www.mdpi.com/2076-3417/14/23/11451high-density road networksmultiscale visualizationroad selectionraster datasuperpixel segmentation |
| spellingShingle | Yilang Shen Yiqing Zhang Renzhu Li Selection of Grid Road Networks Based on Raster Data Applied Sciences high-density road networks multiscale visualization road selection raster data superpixel segmentation |
| title | Selection of Grid Road Networks Based on Raster Data |
| title_full | Selection of Grid Road Networks Based on Raster Data |
| title_fullStr | Selection of Grid Road Networks Based on Raster Data |
| title_full_unstemmed | Selection of Grid Road Networks Based on Raster Data |
| title_short | Selection of Grid Road Networks Based on Raster Data |
| title_sort | selection of grid road networks based on raster data |
| topic | high-density road networks multiscale visualization road selection raster data superpixel segmentation |
| url | https://www.mdpi.com/2076-3417/14/23/11451 |
| work_keys_str_mv | AT yilangshen selectionofgridroadnetworksbasedonrasterdata AT yiqingzhang selectionofgridroadnetworksbasedonrasterdata AT renzhuli selectionofgridroadnetworksbasedonrasterdata |