Volume 39 Issue 5
May  2014
Turn off MathJax
Article Contents
Abstract
Related Articles
TIAN Jing, HE Qingsong, YAN Fen. Formalization and New Algorithm of stroke Generation in Road Networks[J]. Geomatics and Information Science of Wuhan University, 2014, 39(5): 556-560. DOI: 10.13203/j.whugis20120127
Citation: TIAN Jing, HE Qingsong, YAN Fen. Formalization and New Algorithm of stroke Generation in Road Networks[J]. Geomatics and Information Science of Wuhan University, 2014, 39(5): 556-560. DOI: 10.13203/j.whugis20120127
shu

Formalization and New Algorithm of stroke Generation in Road Networks

  • 1School of Resource and Environmental Sciences,Wuhan University,Wuhan 430079,China;2Key Laboratory of Geographic Information System,Ministry of Education,Wuhan University,Wuhan 430079,China

Funds: National Science Foundation for Fostering Talents in Basic Research of the National Natural Science Foundation ofChina,No.J1103409.
More Information
  • Author Bio:

    TIAN Jing,PhD,lecturer,specializes in automated map generalization and spatial data mining.

  • Received Date: March 19, 2013
  • Revised Date: May 04, 2014
  • Published Date: May 04, 2014
    Graphical Abstract
    • Abstract
  • Objective Road network generalization and analysis have been hot issues in the geographical informa-tion science.The stroke in a road network is defined as a set of one or more road segment in a non-branching,connected chain,based on the good continuity principle.The stroke plays an importantrole in road network generalization,analysis,pattern recognition and schematic map generation.Ex-isting studies focus on the stroke generation algorithms.However,the formalization of stroke genera-tion at the conceptual level is absent.This paper first formalizes the stroke generation as clusteringproblem,and then presents a hierarchical clustering based stroke generation algorithm.Time com-plexity and some properties of the proposed algorithm are analyzed in detail.Finally,the algorithm isverified using the Shenzhen road network at the 1∶50 000scale.
    • FullText(HTML)
    • References (0)
    • Related Articles

  • Related Articles

    [1]Chen Xinyang, Long Xiaoxiang, Li Qingpeng, Li Jingmei, Han Qijin, Xu Zhaopeng, Yao Weiyuan. Data Proccing and Accuracy Verification for Laser Altimeter of Terrestrial Ecosystem Carbon Inventory Satellite[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230110
    [2]ZHOU Ping, TANG Xinming, WANG Xia, LIU Changru, WANG Zhenming. Geometric Accuracy Evaluation Model of Domestic Push-Broom Mapping Satellite Image[J]. Geomatics and Information Science of Wuhan University, 2018, 43(11): 1628-1634. DOI: 10.13203/j.whugis20160486
    [3]YAN Wei, LIU Jianjun, REN Xin, WANG Fenfei. Accuracy Analysis of CE-3 Moon-Based Ultraviolet Telescope Geometric Positioning[J]. Geomatics and Information Science of Wuhan University, 2018, 43(1): 133-137, 166. DOI: 10.13203/j.whugis20150162
    [4]MENG Weican, ZHU Shulong, CAO Wen, CAO Bincai, GAO Xiang. High Accuracy On-Orbit Geometric Calibration of Linear Push-broom Cameras[J]. Geomatics and Information Science of Wuhan University, 2015, 40(10): 1392-1399,1413. DOI: 10.13203/j.whugis20140534
    [5]YIN Chuan, WANG Yanhui. Target Geometry Matching Threshold in Incremental Updatingof Road Networks Based on OSTU[J]. Geomatics and Information Science of Wuhan University, 2014, 39(9): 1061-1067. DOI: 10.13203/j.whugis20130575
    [6]YAN Li, JIANG Yun, WANG Jun. Building of Rigorous Geometric Processing Model Based onLine-of-Sight Vector of ZY-3 Imagery[J]. Geomatics and Information Science of Wuhan University, 2013, 38(12): 1451-1455.
    [7]LIU Liangming, YE Yuanxin, FAN Dengke, XU Qi. Study on Geometric Rectification for FY-2 S-VISSR Data[J]. Geomatics and Information Science of Wuhan University, 2012, 37(4): 384-388.
    [8]WU Fang, ZHU Kunpeng. Geometric Accuracy Assessment of Linear Features' Simplification Algorithms[J]. Geomatics and Information Science of Wuhan University, 2008, 33(6): 600-603.
    [9]Li Deren, Wang Xinhua. Geometric Calibration of CCD Array Camera[J]. Geomatics and Information Science of Wuhan University, 1997, 22(4): 308-313,317.
    [10]Fan Yonghong. Geometric Rectification of SAR Image[J]. Geomatics and Information Science of Wuhan University, 1997, 22(1): 39-41.

  • Cited by

    Periodical cited type(32)

    1. 王蕾，何鑫，廖成. 基于知识库相似检索的自然资源调查监测图斑辅助辨识方法. 测绘通报. 2025(02): 137-142 .
    2. 张秀锦，张秀民. 基于轻便的node.js地图识别模型实现分析. 山东交通科技. 2024(02): 130-132 .
    3. 汤冻，奚晓轶，闫涛. 一种用于电视节目播出异态识别的人工智能模型训练方法. 电视技术. 2023(01): 61-65 .
    4. 梁生珺，于明鑫. 应用于无人机平台的轻量Transformer排水口检测框架. 电子技术与软件工程. 2023(01): 165-168 .
    5. 陶立清，黄国满，杨书成，王童童，盛辉军，范海涛. 一种利用卷积神经网络的干涉图去噪方法. 武汉大学学报(信息科学版). 2023(04): 559-567 .
    6. 桂志鹏，胡晓辉，刘欣婕，凌志鹏，姜屿涵，吴华意. 顾及地理语义的地图检索意图形式化表达与识别. 地球信息科学学报. 2023(06): 1186-1201 .
    7. 李从初，励臣儒，朱佳敏，姚浩立. 基于迁移学习和Xception网络的海雾能见度等级估测研究. 浙江气象. 2023(01): 23-28 .
    8. 田启川，吴施瑶，马英楠. 基于卷积神经网络的光学遥感影像分析综述. 计算机应用与软件. 2023(10): 1-9+45 .
    9. 樊翔宇，张聪，杨柳. 融合梅尔谱和循环残差的小样本音频分类模型. 计算机仿真. 2022(02): 195-202 .
    10. 金海峰，吴楠，张悠然. 智慧家庭中的人体动作识别研究综述. 软件导刊. 2022(04): 240-247 .
    11. 冯新扬，邵超. 跨卷积网络特征融合的SAR图像目标识别. 系统仿真学报. 2021(03): 554-561 .
    12. 任加新，刘万增，李志林，李然，翟曦. 利用卷积神经网络进行“问题地图”智能检测. 武汉大学学报(信息科学版). 2021(04): 570-577 .
    13. 王建华，冉煜琨. 适用于便携式设备的深度神经网络眼动跟踪. 计算机与现代化. 2021(08): 58-63 .
    14. 郑雯，沈琪浩，任佳. 基于Improved DR-Net算法的糖尿病视网膜病变识别与分级. 光学学报. 2021(22): 72-83 .
    15. 任福，侯宛玥. 面向机器阅读的地图名称注记类别识别方法. 武汉大学学报(信息科学版). 2020(02): 273-280 .
    16. 吴晓玲，黄金雪，何文海. 基于深度卷积神经网络的塑料垃圾分类研究. 塑料科技. 2020(04): 86-89 .
    17. 叶宇光. 基于深度残差网络的图像识别技术研究. 韶关学院学报. 2020(06): 18-22 .
    18. 王科举，廉小亲，陈彦铭，安飒，龚永罡. 基于深度学习的机械臂视觉系统. 信息技术与信息化. 2020(08): 203-208 .
    19. 侯东阳，武昊，陈军. 时空数据Web搜索的研究进展. 地理信息世界. 2020(04): 1-12+21 .
    20. 刘彩玲，岳荷荷. 基于(2D)~2-PCANet的种子图像识别. 计算机应用与软件. 2020(10): 232-238 .
    21. 谢万里，李宏志，周辉，尹绍武. 基于迁移学习与卷积神经网络的鱼濒死预警系统研究. 中国农机化学报. 2019(02): 186-192 .
    22. 宋益盛，林志杰. 基于迁移学习和数据增强技术的物种识别. 现代计算机. 2019(14): 57-63 .
    23. 李雄，文开福，钟小明，杨辉，秦德浩. 基于深度学习的人脸识别考勤管理系统开发. 实验室研究与探索. 2019(07): 115-118+123 .
    24. 李静，韩震，王文柳，崔艳荣. 基于OverFeat模型的长江口南汇潮滩植被分类. 生态科学. 2019(04): 135-141 .
    25. 江涛，王新杰. 基于卷积神经网络的高分二号影像林分类型分类. 北京林业大学学报. 2019(09): 20-29 .
    26. 呙鹏程，吴礼洋. 融合卷积特征与判别字典学习的低截获概率雷达信号识别. 兵工学报. 2019(09): 1881-1889 .
    27. 刘洋，冯全，王书志. 基于轻量级CNN的植物病害识别方法及移动端应用. 农业工程学报. 2019(17): 194-204 .
    28. 门计林，刘越岩，张斌，周繁. 多结构卷积神经网络特征级联的高分影像土地利用分类. 武汉大学学报(信息科学版). 2019(12): 1841-1848 .
    29. 赵波，廖坤，邓春宇，谈元鹏，曹生现. 基于卷积神经学习的光伏板积灰状态识别与分析. 中国电机工程学报. 2019(23): 6981-6989+7111 .
    30. 尹宗天，谢超逸，刘苏宜，刘新如. 低分辨率图像的细节还原. 软件. 2018(05): 199-202 .
    31. 宋俊芳. 基于BP神经网络的图像分割. 数字通信世界. 2018(03): 66+170 .
    32. 朱祺夫，赵俊三，陈磊士，李易. 基于深度学习的遥感影像城市建筑用地提取. 软件导刊. 2018(10): 18-21 .

    Other cited types(62)

Catalog

    Get Citation
    PDF
    XML
    Article views PDF downloads Cited by(94)
    Related

    Copyright © 2013 《武汉大学学报·信息科学版》编辑部

    Address：武汉大学文理学部本科生院楼北5楼Post Code：430072

    Tel：027-68778465,68788631E-mail:whuxxb@vip.163.com

    Submit Article

    Submission Guidelines

    e

    Contact Us

    Qrcode

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return