基于3S技术的收获期农机调度

农作物系统本身具有显著的时空变异性,忽略作物成熟日期的差异采取相同的收获日期可能会造成实际收获产量、质量的损失,使用3S技术进行科学合理的农机调度是减少这一损失的重要前提。本文使用获取的遥感数据反演了作物的成熟日期,并在分析了农机时空调度的依据和限制条件的基础上,提出了农机时空调度问题,使用改进的遗传算法构建了农机时空调度模型和算法并使用MATLAB进行了程序实现,最后构建了基于3S技术的农机调度技术框架。     

电子政务空间辅助决策数据调度方法研究与实践

本文总结了数据调度的主要方法,分析了电子政务空间辅助决策的数据调度要求.结合电子政务试点项目实践提出了"混合型"数据调度结构框架与实现方法,利用服务器与客户器集成,支持服务器方式、客户端和服务器与客户器协同三种方式数据调度。     

大规模矢量数据分块调度策略研究

提出了一种用于大规模矢量数据调度的策略,引入基于"金字塔"思想的数据调度和工作空间的服务配置技术,采用WFS服务的方式调度数据,将GML数据解析为坐标数据进行展示。在Gaea平台上实现了该策略,对实验结果进行分析,结果表明该策略在矢量数据调度方面有一定的改进。     

三维GIS中海量模型调度与存储

城市级三维GIS管理系统需要管理海量的三维模型数据,要想实现高效、稳定的海量建筑模型渲染,需要研究一套行之有效的模型调度方式和数据存储方案。通过总结实际系统建设过程中遇到的各种问题,研究出了使用数据格网调度模型和打包存储模型数据的方法,大幅提高了三维GIS中海量模型的调度效率。     

遥感影像处理特征及数据密集型任务调度算法研究

针对云计算技术在遥感影像处理中的应用问题,对于其任务特征以及调度问题进行了研究。首先定义了遥感影像处理工作流,并在特征分析的基础上对任务类型进行了划分,将任务划分为数据密集型和计算密集型两种。针对数据密集型任务调度问题,通过建立流程间的数据依赖度矩阵和数据集划分制定调度策略,设计了以减少数据传输次数为目标的调度算法。实验结果表明,该算法与随机算法相比,可以有效减少数据集、数据服务器数量和不同比例的固定数据集等多种情况下的数据传输次数,该算法能够满足常用数据密集型任务的调度需求,如计算航段信息、计算缩略图同名像点等。     

分布式环境下海量瓦片数据实时组织与调度策略研究

针对现有分布式环境下海量瓦片数据组织与调度策略中未形成一种通用的海量瓦片数据管理方法、瓦片数据的预切割技术存在严重缺陷两个问题,提出一种通用的海量瓦片数据的分布式实时组织、调度策略,摒弃传统瓦片数据的全程预切割过程,从数据部署上采用预切割与实时处理相融合的机制,在高级别级数上通过实时缓冲的方式对分布式海量瓦片数据进行管理。通过具体实例,对海量瓦片数据的组织与分布式部署、海量瓦片数据的实时组织与调度过程进行详细阐述,并解决这两个缺陷,实现了海量瓦片数据的分布式实时组织与调度。     

数字城市建设中GIS数据跨服务器无缝漫游

针对当前数字城市建设中跨服务器数据调度的问题,以NewMap在城市三维场景数据跨服务器动态调度的应用为例,探讨基于服务器独立配置的数据跨服务器动态调度机制.通过Server Layer的引入实现数据跨服务器调度中对未知服务器节点的自主查询与数据加载,以及基于Server Tree的服务器节点整体维护.最后通过试验验证该机制在跨服务器数据动态调度中的可行性及有效性.     

三维环境下栅格数据的动态调度

提出了一个通过数据缓冲区和多线程调度的算法,实现了大范围栅格数据的实时动态调度,提高了地形数据三维可视化表达的效率,在实践应用中取得了很好的效果。     

基于三维GIS的海量地形数据存储和调度的研究

地形数据用来表达地形起伏特征,如何有效地存储和调度海量地形数据是三维GIS研究热点之一。本文基于金字塔影像、分片分块的方法组织三维地形数据,以此基础开发了基于三维显示平台和通用数据库(DBMS)的空间数据中间件,通过空间数据中间件对数据库进行地形数据的读写,实现了三维地形数据的存储和调度。     

多媒体通信技术在应急指挥地理信息系统中的应用

随着地理信息系统(GIS)在各行各业应用的不断深入,地理信息系统需要与各种各样格式和内容的多媒体信息相结合,为应用部门的用户提供以空间位置为载体,对多媒体数据进行管理和调度的多媒体信息处理技术功能。本文主要以应急指挥地理信息系统为例,从系统总体结构上介绍如何把种类繁多的多媒体信息数据与空间数据相结合,如何对文字、图片、视频等信息组织入库,利用Microsoft Windows CE for Visual C/C++工具研发PDA(Personal Digital Assistant)电子地图子系统、PDA应急事件信息录入软件;以及如何在Internet或Intranet网络上对多媒体信息进行调度发布等多媒体通信技术。     

Behavior mining of spatial objects with data field

The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining.     

基于数据场的空间目标行为挖掘(英文)

The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining. Supported by the National 973 Program of China(No.2006CB701305,No.2007CB310804), the National Natural Science Fundation of China (No.60743001), the Best National Thesis Fundation (No.2005047), the National New Century Excellent Talent Fundation (No.NCET-06-0618).     

基于GDI+的多波束测深数据可视化研究

多波束测深数据的可视化处理是提高数据处理效率和精度的有效途径。本文针对多波束测深数据的密集、数据量大的特点,探讨了基于GDI+实现多波束测深数据处理可视化的基本流程和关键步骤,给出了几种实用的坐标变换模式,提出基于图像的绘图模式实现了多波束测深数据的快速绘制和刷新,提高了绘制和刷新速度,满足了测深数据可视化处理的要求。     

不同空间数据格式的数据量实验研究

针对一些通用的空间数据的数据交换格式进行了研究，并就不同数据格式的数据量进行了对比实验。结果表明，在栅格数据中，．bmp格式的数据量最大，.jpg格式和．gif格式的数据量较小；矢量格式的数据量的大小与空间数据的属性有关，在几种常用的矢量格式中，．e00格式的数据量最大。     

一种适于Compass周跳探测的三频数据优化组合

根据多频数据组合原理,推导了组合观测值电离层比例因子与噪声比例因子的关系,分析了Compass码和相位的多种可能组合,从中选取3种线性无关的优化组合作为周跳检验量。所选检验量保持了周跳的整数特性,便于准确估计与修复周跳。利用Compass-M1实测数据进行的实验表明,基于优化组合可以探测出1周以上的所有周跳,在缺失历元较少和无电离层异常时可以准确估计与修复基本频率的周跳。     

有关GPS RTK差分数据传输技术探讨

探讨了GPS RTK差分数据的传输技术问题。通过分析RTK数据传输所采用UHF波特点、传输电文机理及影响传输距离的因素,研究了UHF波用于RTK通信的不足,并给出了扩频技术与软件无线电技术两种改进技术。     

Impact of discretization methods on the rough set-based classification of remotely sensed images

Abstract

In recent years, the rough set (RS) method has been in common use for remote-sensing classification, which provides one of the techniques of information extraction for Digital Earth. The discretization of remotely sensed data is an important data preprocessing approach in classical RS-based remote-sensing classification. Appropriate discretization methods can improve the adaptability of the classification rules and increase the accuracy of the remote-sensing classification. To assess the performance of discretization methods this article adopts three indicators, which are the compression capability indicator (CCI), consistency indicator (CI), and number of the cut points (NCP). An appropriate discretization method for the RS-based classification of a given remotely sensed image can be found by comparing the values of the three indicators and the classification accuracies of the discretized remotely sensed images obtained with the different discretization methods. To investigate the effectiveness of our method, this article applies three discretization methods of the Entropy/MDL, Naive, and SemiNaive to a TM image and three indicators for these discretization methods are then calculated. After comparing the three indicators and the classification accuracies of the discretized remotely sensed images, it has been found that the SemiNaive method significantly reduces large quantities of data and also keeps satisfactory classification accuracy.     

Big data analytics: six techniques

Abstract

Big data have 4V characteristics of volume, variety, velocity, and veracity, which authentically calls for big data analytics. However, what are the dominant characteristics of big data analysis? Here, the analytics is related to the entire methodology rather than the individual specific analysis. In this paper, six techniques concerning big data analytics are proposed, which include: (1) Ensemble analysis related to a large volume of data, (2) Association analysis related to unknown data sampling, (3) High-dimensional analysis related to a variety of data, (4) Deep analysis related to the veracity of data, (5) Precision analysis related to the veracity of data, and (6) Divide-and-conquer analysis related to the velocity of data. The essential of big data analytics is the structural analysis of big data in an optimal criterion of physics, computation, and human cognition. Fundamentally, two theoretical challenges, ie the violation of independent and identical distribution, and the extension of general set-theory, are posed. In particular, we have illustrated three kinds of association in geographical big data, ie geometrical associations in space and time, spatiotemporal correlations in statistics, and space-time relations in semantics. Furthermore, we have illustrated three kinds of spatiotemporal data analysis, ie measurement (observation) adjustment of geometrical quantities, human spatial behavior analysis with trajectories, data assimilation of physical models and various observations, from which spatiotemporal big data analysis may be largely derived.     

Local Solutions to Inverse Problems in Geodesy

In many geoscientific applications, one needs to recover the quantities of interest from indirect observations blurred by colored noise. Such quantities often correspond to the values of bounded linear functionals acting on the solution of some observation equation. For example, various quantities are derived from harmonic coefficients of the Earth’s gravity potential. Each such coefficient is the value of the corresponding linear functional. The goal of this paper is to discuss new means to use information about the noise covariance structure, which allows order-optimal estimation of the functionals of interest and does not involve a covariance operator directly in the estimation process. It is done on the basis of a balancing principle for the choice of the regularization parameter, which is new in geoscientific applications. A number of tests demonstrate its applicability. In particular, we could find appropriate regularization parameters by knowing a small part of the gravitational field on the Earth’s surface with high precision and reconstructing the rest globally by downward continuation from satellite data.     

海量矢量地图数据网络发布的引擎开发与应用实践

聚焦“地理信息公共服务平台”建设中大数据量矢量图层数据管理与发布的问题,介绍了一种在网络上快速发布大数据量（具体指“图层数多、单个图层数据量大”）矢量图层数据的新方法及其应用实践,详细描述了“海量矢量地图数据网络发布引擎”的开发背景、所解决的问题、实现原理以及应用实效等内容。