在服务国土资源管理改革创新中释放信息化正能量

以党中央关于信息化工作的战略部署为统领,研判新一代信息技术对信息化建设与应用服务模式带来的革新,综合分析信息化对于服务保障国土资源管理已取得的显著成效,以及今后行政体制改革方向和生态文明建设新要求,预示着在国土资源管理改革创新中还蕴涵着巨大的信息化红利,基于"一张图"和"三大平台"在可预见的将来仍不会过时的基本判断,并将其作为逻辑起点,以国土资源管理改革创新业务目标为导向、以构建"智慧国土"为愿景、以新一代信息技术与国土资源管理业务深度融合为措施,提出全面升级国土资源信息化体系的基本思路和途径。     

CVVF方法用于GPS多路径效应的研究

将交叉证认法与Vondrak数字滤波器相组合,提出一种分离测量资料中信号与噪声的新方法,即CVVF方法,并将该方法用于GPS多路径效应的研究中.对数字模拟试验和实际观测资料的分析表明,该方法能最大限度地削弱测量的随机误差,使资料序列中的信号和噪声合理分离.同时,利用GPS多路径效应周期性重复的特性,可有效地削弱多路径效应对观测结果的影响,从而提高GPS定位精度.     

利用GIS技术和RS技术减灾防灾

减灾防灾是一项需要多学科知识融会贯通的长期工程。灾难的发生是由多种因素造成的,在分析管理的时候,需要大量的数据源,因而,分析过程相当地耗时耗费。近年,由于能够高效提供详细、精确的相关数据．GIS技术和RS技术在灾难管理方面应用广泛、发展迅速,遥感技术提供灾情数据．GIS技术通过模型将各种数据有机地整合起来,包括空间数据、非空间数据、属性数据,使它们在灾难管理的各个阶段发挥不同的作用。     

基于ObjectARX技术的AutoCAD数据格式转换

概要介绍了ObjectARX开发环境以及ARX应用程序,重点论述了如何在ObjectARX开发环境下,编写ARX应用程序,实现对AutoCAD数据库的访问,及对AutoCAD数据进行提取和格式转换的方法,给出了具体的实现思路和实现的代码细节。     

基于MultiGen Creator构建三维数字小区的理论与实践

在阐述数字小区构建意义的基础上,结合MultiGen Creator专业建模软件和Vega可视化仿真应用软件,详细论述了构建三维数字小区的理论与实践,重点阐述了小区场景中对象的分类以及各自的建模理论,并对典型的两种实体在实践上的构建技术具体描述了其在Creator中的实现步骤,体现了该软件在建模方面显著节约面片的功能以及所建模型在Vega系统中实时顺畅显示的优越性。     

南京长江三桥钢索塔拼装测量控制网的建立

南京长江三桥是我国首座主塔采用钢结构形式的特大型斜拉桥。根据钢索塔拼装测量的精度要求,介绍了该桥首级控制网的概况,对钢索塔架设专用控制网的网形选择、方案设计、观测方法和数据处理进行了探讨。实践证明,所采取的专用控制网布设方法和测量方案是合理的,对今后类似工程具有借鉴意义。     

一种新的二进制地图矢量文件格式设计

比较了二制文件与文本文件的优点,以线状数据为例设计了便于文档序列化,易于面向对象ＧＩＳ进行了的二进矢量地图文件格式。     

Enhanced orbit determination for BeiDou satellites with FengYun-3C onboard GNSS data

GPS Solutions - A key limitation for precise orbit determination of BeiDou satellites, particularly for satellites in geostationary orbit (GEO), is the relative weak geometry of ground stations....     

Integrated compression of vehicle spatio‐temporal trajectories under the road stroke network constraint

With fast growth of all kinds of trajectory datasets, how to effectively manage the trajectory data of moving objects has received a lot of attention. This study proposes a spatio‐temporal data integrated compression method of vehicle trajectories based on stroke paths coding compression under the road stroke network constraint. The road stroke network is first constructed according to the principle of continuous coherence in Gestalt psychology, and then two types of Huffman tree—a road strokes Huffman tree and a stroke paths Huffman tree—are built, based respectively on the importance function of road strokes and vehicle visiting frequency of stroke paths. After the vehicle trajectories are map matched to the spatial paths in the road network, the Huffman codes of the road strokes and stroke paths are used to compress the trajectory spatial paths. An opening window algorithm is used to simplify the trajectory temporal data depicted on a time–distance polyline by setting the maximum allowable speed difference as the threshold. Through analysis of the relative spatio‐temporal relationship between the preceding and latter feature tracking points, the spatio‐temporal data of the feature tracking points are all converted to binary codes together, accordingly achieving integrated compression of trajectory spatio‐temporal data. A series of comparative experiments between the proposed method and representative state‐of‐the‐art methods are carried out on a real massive taxi trajectory dataset from five aspects, and the experimental results indicate that our method has the highest compression ratio. Meanwhile, this method also has favorable performance in other aspects: compression and decompression time overhead, storage space overhead, and historical dataset training time overhead.     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.