| 道路网多尺度选取表达:问题、方法与应用:issues, approaches and applications |
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2020-08-02 00:00:00 |
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道路网多尺度选取表达:问题、方法与应用:issues, approaches and applications 本书特色
空间数据多尺度表达已被广泛地应用于电子地图导航与定位、空间数据挖掘与分析。如何快速地表达与更新多尺度空间数据，仍是现有应用中的核心瓶颈问题。
道路网数据不仅是地图上*重要的地理要素之一，而且是地图导航与定位所需的空间数据。本书全面、系统地探讨了道路网数据多尺度选取表达的关键研究问题、核心方法及其典型应用，并通过一系列的实验，比较、分析了各种方法的优劣。
本书可供地图制图学与地理信息工程、地理学与地理信息系统专业的本科生，测绘科学与技术、地理学专业的研究生，以及测绘相关专业的研究人员与工程技术人员阅读参考。
道路网多尺度选取表达:问题、方法与应用:issues, approaches and applications 内容简介
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道路网多尺度选取表达:问题、方法与应用:issues, approaches and applications 目录
chapter 1introduction(1)1.1multiscale spatial representation(1)1.2motivation: the need for automated multiscale spatial representation(2)1.3state of the art on multiscale spatial representation(4)1.4objectives and scope of this book(9)1.5structure of this book(10)chapter 2state of the art on selective omission in a road network(12)2.1issues on selective omission in a road network(12)2.2approaches to selective omission in a road network(21)chapter 3experimental analysis of various issues on selective omission in a road network(39)3.1comparison of various strategies to concatenate road segments into strokes(39)3.2comparison of various measures to determine the importance of individual roads(60)3.3determination of thresholds for selective omission parameters(74)chapter 4experimental analysis of various approaches to selective omission in a road network(90)4.1comparison of general approaches to selective omission in a road network(90)4.2comparison of machine learning approaches to selective omission in a road network(97)4.3investigation of how many training samples are needed by using a machine learning approach to selective omission(110)chapter 5applications(119)5.1construction of road hierarchies for continuous multiscale selective omission(119)5.2use of a machine learning approach for road network updating(128)5.3summary(134)chapter 6summary and outlook(137)6.1summary of this book(137)6.2limitations(140)6.3future research issues(142)references(144)
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