顾及用户体验的三维城市模型自适应组织方法

传统基于“图层付象”的组织方法,没有考虑三维城市模型的不同内容以及不同细节层次的粒度差异,导致在网络环境下的传输效率低,难以满足多用户并发访问的流畅可视化。深入分析了大范围漫游与小范围聚焦的用户体验特点,通过元数据统筹管理和对象离散化,即时响应用户请求,减少无效数据传输,保证了多用户并发环境下的高效调度和浏览。针对模型LoI)和分解的对象,设计了结构统一的对象ID,隐式存储关联关系并支持分布式模型存储管理。以分布式数据库MongoDB为平台进行实验,验证了本文方法的可行性和有效性。     

基于SMVF的高频雷达自适应多目标检测

针对高频雷达强海杂波和干扰环境下的多目标检测问题,结合S-method时频分布和Vitcrbi算法提出了一种多分量线性调频信号瞬时频率估计方法—SMVF法(S-method viterbi fitting),然后根据所提取的瞬时频率的直线拟合性能,提出了一种自适应多目标检测算法。实测数据的处理结果,表明了该算法在低信噪比时,与WDM法(wigncr distribution maxima) , WDV法( wigncr distribution vitcrbi)相比,估计精度提高约2000,验证了该算法的可行性和有效性。     

《昆明市城市排水系统分布专题地图》的编制研究

本文就利用ARCMAP软件编制<昆明城市排水系统分布专题地图>的实践过程,尝试将地图编制与排水管线地理信息系统建设实现很好的结合,实现图-库一体化联接,并最终实现以地下管线探测数据的地图可视化过程,从而完成专业管线专题地图的编制,为城市排水规划、建设与决策提供直观依据.     

国外测绘业发展现状与我们的对策

介绍了欧美等国家的基础测绘管理、测绘投资和国家空间信息政策的情况。归纳出几个发达国家测绘业发展的主要特点 ,比较分析了我国测绘业发展的主要差距 ,提出加速发展中国测绘业的对策、建议。     

定位参数抗差估计的无偏性

根据抗差估计中误差的最基本假设一对称分布,考虑到ρ函数的条件及把反对称估计作为抗差估计计算迭代的初始值,验证了定位参数的抗差估计值也一定是反对称估计,满足无偏性条件,得到定位参数的抗差估计是无偏估计.     

基于MAPX的MapInfo数据空间索引的建立

空间索引技术在诸多领域均有十分重要的研究意义,目前已存在很多种建立方法,各有优缺点。MapInfo软件功能强大易用,但不具有建立空间索引这项功能,因此本文主要研究在MAPX基础上建立一种适合MapInfo数据的空间索引。该方法主要针对车载导航系统中的一个基本算法地图匹配来进行研究。采用单层规则格网建立的空间索引并对其进行改进,在第一步划分的基础上再次进行网格划分,建立起两级交错式划分方法的空间索引。通过实验表明该方法使实时路段搜索过程的实现简单、方便、明了。     

A 3-dimensional data model for visualizing cloverleaf junction in a city model

The research work has been seldom done about cloverleaf junction expression in a 3-dimensional city model (3DCM). The main reason is that the cloverleaf junction is often in a complex and enormous construction. Its main body is bestraddle in air, and has aerial intersections between its parts. This complex feature made cloverleaf junction quite different from buildings and terrain, therefore, it is difficult to express this kind of spatial objects in the same way as for buildings and terrain. In this paper, authors analyze spatial characteristics of cloverleaf junction, propose an all-constraint points TIN algorithm to partition cloverleaf junction road surface, and develop a method to visualize cloverleaf junction road, surface using TIN. In order to manage cloverleaf junction data efficiently, the authors also analyzed the mechanism of 3DCM data management, extended BLOB type in relational database, and combined R-tree index to manage 3D spatial data. Based on this extension, an appropriate data structure for cloverleaf junction in 3DCM is proposed.     

大跨钢桁梁形变对轨道平顺性的影响

针对由于大跨钢桁梁形变导致其上设置的轨道控制点(CPIII)空间位置的变化,进而导致轨道平顺性的测量结果受到影响的问题,该文从轨道维护角度出发,探讨大跨钢桁梁形变对轨道测量及平顺性影响规律。通过模拟实验对大跨钢桁梁形变引起的自由设站坐标、钢轨几何位置测量值以及轨道平顺性指标3方面的影响进行了深入讨论,结果表明:对于直线段大跨钢桁梁部分的轨道平顺性影响较小,对钢桁梁与两端混凝土梁连接处的轨道平顺性影响较大。     

Point grid map: a new type of thematic map for statistical data associated with geographic points

ABSTRACT

Social, economic, and environmental statistical data associated with geographic points are currently globally available in large amounts. When conventional thematic maps, such as proportional symbol maps or point diagram maps, are used to represent these data, the maps appear cluttered if the point data volumes are relatively large or cover a relatively dense region. To overcome these limitations, we propose a new type of thematic map for statistical data associated with geographic points: the point grid map. In a point grid map, an input point data set is transformed into a grid in which each point is represented by a square grid cell of equal size while preserving the relative position of each point, which leads to a clear and uncluttered appearance, and the grid cells can be shaded or patterned with symbols or diagrams according to the attributes of the points. We present an algorithm to construct a point grid map and test it with several simulated and real data sets. Furthermore, we present some variants of the point grid map.     

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.