高坪-吉心段公路喀斯特工程地质综合勘察

以沪蓉国道主干线湖北省高坪-吉心段的喀斯特地质勘察工作为例，探讨喀斯特地区公路工程地质综合勘察的基本方法。在喀斯特地区运用遥感和地理信息技术技术、地质测绘、综合物探以及地质钻探等勘察手段，通过综合分析各种勘探成果，能够较准确地查明可溶岩的分布、喀斯特形态类型、分布规律、控制因素以及喀斯特水的赋存、分布和运移规律等，并根据不同的喀斯特地貌提出了有针对性的处理对策。通过不断的实践和经验总结。针对喀斯特工程地质问题合理运用综合工程勘察方法，能做出科学的分析评价，可以适应喀斯特区公路建设的需要，并且供今后在喀斯特地区修建公路时参考和借鉴。     

LIDAR测量技术在公路测设中的应用分析

公路是线性工程,适于航测与LIDAR技术进行测设,以提供DLG,DEM,DOM产品。航空摄影后处理周期较长,需要几个月甚至更长时间,影响测设任务进程,而LIDAR技术能够弥补这些缺陷,提高工程效率的同时提高工程质量。本文采用LIDAR技术进行分析,并且与以往的航测摄影进行比较,得出一些有益的成果。     

面向道路网络地图示意化的线形简化方法

针对当前示意网络地图线形状简化方法中存在需要人工干预或地图认知度低的问题,本文提出自动化程度高并能保证良好地图认知的线形状简化方法.该方法通过合理设定线形状简化中所需的阈值以避免人工干预,提高了线形状简化过程的自动化程度,并通过在简化过程中应用平滑算法,以减少示意结果中线对象在方向上的转折数,从而提高了简化度和清晰度.试验表明该方法不仅可以生成清晰的示意结果,并且能够保证良好的地图认知度.     

基于数字地形的三维道路快速建模与应用

介绍一种在高精度三维地形模型的基础上,通过导入道路规划设计快速生成三维道路模型,并进行土石方量计算等量化分析的方法。基于三维道路模型,可以开展各类可视化分析和规划管理工作,为非专业人士理解规划设计意图提供了直观的方式。最后,以典型示范应用为例,说明研究成果可应用的领域和效果。     

道路工程GPS平面控制网建网方法

以往GPS建立的平面坐标系属法线系统,而选线设计均在垂线和水准面为基准的坐标系统下进行,其数据未做两化改正而直接用于施工放样。为了消除因GPS引入线路勘测而引发的法线系统坐标系与垂线系统坐标系之间的矛盾,本文采用逆向两化改正的方法,从理论上提出了直接用GPS建立垂线和水准面系统下平面坐标系的技术路线,并推导出逆向两化改正及其精度评定公式。     

Enrichment of topographic road database for the purpose of routing and navigation

With growing demand on multi-purpose or multi-modal navigation, the route calculation needs to traverse semantically enriched road networks for different transportation modes. Currently, operational route planning algorithms reveal rather limited performances or their potential for comprehensive applications are constrained by the unavailable or insufficient interoperation among the underlying geo-data that are separately maintained in different spatial databases. To overcome this limitation, a novel approach has been proposed to integrate the routing-relevant information from different data sources, which involves three processes: (1) automatic matching to identify the corresponding road objects between different datasets; (2) interaction to refine the automatic matching result; and (3) transferring the routing-relevant information from one data-set to another. In process (1), the Delimited Stroke Oriented algorithm is employed to achieve the automatic data matching between different datasets, which has revealed a high matching rate and certainty. However uncertain matching problems occur in areas where topological conditions are too complicated or inconsistent. The remaining unmatched or wrongly matched objects are treated in process (2), with the help of a series of interaction tools. On the basis of refined matching results after the interaction, process (3) is dedicated to automatic integration of the routing-relevant information from different data sources.     

粗精匹配结合的矢量道路辅助惯性导航算法

针对无源环境下无法采用卫星导航定位方式对惯性导航系统的累积误差进行修正的问题,提出一种采用高精度的矢量道路数据进行粗精匹配的导航校正算法。该方法首先通过分析惯导轨迹的特征标示点以及外接矩形,剔除明显的误匹配道路和冗余道路,获取待匹配道路集合;进而,结合ICCP算法具有匹配精度较高,匹配结果较稳定的优点,完成惯导轨迹位置误差的补偿和校正;最后,根据匹配方差和历史匹配轨迹对匹配结果的准确性进行分析和判断。仿真实验结果表明,该算法能够提高惯性导航定位误差的校正精度,减少在复杂道路交叉口等情况的误匹配。     

Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery

Accurate information on the conditions of road asphalt is necessary for economic development and transportation management. In this study, object-based image analysis (OBIA) rule-sets are proposed based on feature selection technique to extract road asphalt conditions (good and poor) using WorldView-2 (WV-2) satellite data. Different feature selection techniques, including support vector machine (SVM), random forest (RF) and chi-square (CHI) are evaluated to indicate the most effective algorithm to identify the best set of OBIA attributes (spatial, spectral, textural and colour). The chi-square algorithm outperformed SVM and RF techniques. The classification result based on CHI algorithm achieved an overall accuracy of 83.19% for the training image (first site). Furthermore, the proposed model was used to examine its performance in different areas; and it achieved accuracy levels of 83.44, 87.80 and 80.26% for the different selected areas. Therefore, the selected method can be potentially useful for detecting road conditions based on WV-2 images.     

A Comparative Study of Various Supervised Learning Approaches to Selective Omission in a Road Network

Selective omission is necessary for road network generalisation. This study investigates the use of supervised learning approaches for selective omission in a road network. To be specific, at first, the properties to measure the importance of a road in the network are viewed as input attributes, and the decision of such a road is retained or not at a specific scale is viewed as an output class; then, a number of samples with known input and output are used to train a classifier; finally, this classifier can be used to determine whether other roads to be retained or not. In this study, a total of nine supervised learning approaches, i.e., ID3, C4·5, CRT, Random Tree, support vector machine (SVM), naive Bayes (NB), K-nearest neighbour (KNN), multilayer perception (MP) and binary logistic regression (BLR), are applied to three road networks for selective omission. The performances of these approaches are evaluated by both quantitative assessment and visual inception. Results show that: (1) in most cases, these approaches are effective and their classification accuracy is between 70% and 90%; (2) most of these approaches have similar performances, and they do not have any statistically significant difference; (3) but sometimes, ID3 and BLR performs significantly better than NB and SVM; NB and KNN perform significantly worse than MP, SVM and BLR.     

空间句法在开封市区路网空间结构中的应用

基于空间句法模型中的整合度指标计算,借助Arcview与ArcGIS软件的计算和空间分析功能,分析开封市区路网空间的可达性和可达性区域的空间分布特点,对存在问题予以归纳,提出相应的调控建议.