交通图出版前的数据质量控制研究

数据质量控制一直以来都是地理信息系统(GIS)的一个关键环节,随着GIS在地图出版领域的应用,地图出版前的数据质量控制也成为了目前需要迫切解决的问题。本文研究了交通图编制的基本要求以及编制规范,通过GIS数据和交通图出图数据的比较,总结了在交通图出版前有可能存在的数据质量问题,并提出了相应的控制措施。     

Singular value decomposition and cluster analysis as regression diagnostics tools for geodetic applications

It is well known that high-leverage observations significantly affect the estimation of parameters. In geodetic literature, mainly redundancy numbers are used for the detection of single high-leverage observations or of single redundant observations. In this paper a further objective method for the detection of groups of important and less important (and thus redundant) observations is developed. In addition, the parameters which are predominantly affected by these groups of observations are identified. This method thus complements other diagnostics tools, such as, e.g., multiple row diagnostics methods as described in statistical literature (see, e.g., Belsley et al. in Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York, 1980). The method proposed in this paper is based on geometric aspects of adjustment theory and uses the singular value decomposition of the design matrix of an adjustment problem together with cluster analysis methods for regression diagnostics. It can be applied to any geodetic adjustment problem and can be used for the detection of (groups of) observations that significantly affect the estimated parameters or that are of negligible impact. One of the advantages of the proposed method is the improvement of the reliability of observation plans and thus the reduction of the impact of individual observations (and outliers) on the estimated parameters. This is of particular importance for the very long baseline interferometry technique which serves as an application example of the regression diagnostics tool.     

基于要素规则分类的城镇地籍数据建库方法研究

城镇地籍数据的有效分类与正确组织是地籍数据库规范建立和正常运行的前提条件。本文提出了基于地籍要素规则的分类方法,按照地籍数据的表达类型,描述了宗地背景图形数据、宗地图形数据、测量控制点数据、属性数据和档案数据的各个要素定义和编码规则。以成都市地籍数据库的建设为实例,根据各种来源的地籍数据分别开展了不同类型地籍数据的快速分类与入库工作。     

基于全站仪观测数据的三维平差模型及数据处理

建立基于全站仪多维观测数据的三维网平差模型,用VisualBasic语言编制计算程序,使其自动完成控制网的平差,解决实际应用中全站仪采集数据和成果处理中存在的问题,指导测绘生产,使全站仪更方便地用于地形控制测量、工程控制测量等测绘工程中。     

基于模板阴影体算法的矢量数据在三维场景中的绘制

提出一种基于模板阴影体算法的矢量数据绘制方法,实现矢量数据在三维场景中的高质量实时叠加显示。南于该方法基于屏幕空间,所以具有像素级的精度,不会出现传统的基于纹理方法所产生的绘制走样现象;而且不受地形几何数据的约束,其执行效率与地形数据的复杂度无关,仅取决于矢量数据本身的复杂度。详细论述基于模板阴影体算法矢量数据绘制的关键技术,并通过试验验证该方法的有效性。     

建筑物沉降观测技术方法探讨

随着城市建设的迅猛发展,城市规模越来越大,建筑物越来越高,由此带来的安全隐患越来越大,由于高层建筑一般采用桩基基础,且荷载较大,对高层建筑的内部基础和设备的位置关系有很高的精度要求,其施工安装阶段将给高层建筑本身及周边建筑群体带来复杂的形变影响。为了保障建筑物施工和运营的安全,就必须对其进行沉降观测。本文结合笔者多年的实际工作经验,并结合具体的实例,对高层建筑物沉降原因、沉降观测前的准备工作及沉降观测的实施过程和观测数据的处理方法等进行了论述,最后指出如何通过沉降观测技术准确地反映出高层建筑物的沉降情况。     

北京市基础地理信息DLG数据库物理无缝数据层的更新方法探讨

针对北京市基础地理信息DLG数据库物理无缝数据层的更新方案进行了探讨,并介绍了具体更新流程设计及相应的更新编辑实施方法,旨在建立一种高效可行的物理无缝数据更新机制,以减少对不同比例尺每一版更新数据的重复拼接整合工作。     

1：500DLG数据的质量控制与质量评价

DLG数据是城市地形要素的主要表达形式,如何对DLG数据的采集过程进行质量控制、质量检查,如何对DLG数据进行客观的质量评价,是值得探讨和研究的问题。本文针对1：500DLG数据生产的各个环节,全面介绍了其质量控制、质量检查、质量验收、质量评定的内容和方法。     

基于LabView的GPS数据采集及处理

首先介绍全球定位系统(GPS)的基本工作原理及其通常所采用的通信协议,然后阐述了基于LabView的GPS串口数据采集及处理程序的设计思路和主要功能,最后程序实现了对GPS定位信息的采集、存储及对定位信息的实时显示。     

Classifier ensembles for land cover mapping using multitemporal SAR imagery

SAR data are almost independent from weather conditions, and thus are well suited for mapping of seasonally changing variables such as land cover. In regard to recent and upcoming missions, multitemporal and multi-frequency approaches become even more attractive. In the present study, classifier ensembles (i.e., boosted decision tree and random forests) are applied to multi-temporal C-band SAR data, from different study sites and years. A detailed accuracy assessment shows that classifier ensembles, in particularly random forests, outperform standard approaches like a single decision tree and a conventional maximum likelihood classifier by more than 10% independently from the site and year. They reach up to almost 84% of overall accuracy in rural areas with large plots. Visual interpretation confirms the statistical accuracy assessment and reveals that also typical random noise is considerably reduced. In addition the results demonstrate that random forests are less sensitive to the number of training samples and perform well even with only a small number. Random forests are computationally highly efficient and are hence considered very well suited for land cover classifications of future multifrequency and multitemporal stacks of SAR imagery.