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基于地质信息元数据标准的多源空间数据管理系统 总被引:1,自引:0,他引:1
基于对空间数据特点的分析,结合当前的空间数据存储技术,通过对元数据标准的扩展解决多源空间元数据交换的问题;利用单机多空间数据库引擎(spatial database engire,SDE)数据存储技术解决多源空间数据定位及共享的问题;通过建立空间索引数据库,在单机多SDE的基础上,解决空间数据的快速检索定位的问题;从而实现多源空间数据的组织和管理,构建基于地质信息元数据标准的多源空间数据管理系统,为解决资源与环境遥感信息基础平台建设中的空间数据多源、空间数据共享及空间元数据交换打下基础. 相似文献
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分析了现有的空间数据库系统SDE的核心技术和面向对象SDE模型的结构.SDE是目前应用比较成功的空间数据库系统,它采用的空间数据库技术在一定程度上能够很好地对空间数据和属性数据进行管理,对设计自己的空间数据管理系统是很好的借鉴. 相似文献
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基于面向对象的空间数据库引擎模型分析 总被引:5,自引:0,他引:5
分析了现有的空间数据库系统SDE的核心技术和面向对象SDE模型的结构。SDE是目前应用比较成功的空间数据库系统,它采用的空间数据库技术在一定程度上能够很好地对空间数据和属性数据进行管理,对设计自己的空间数据管理系统是很好的借鉴。 相似文献
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为加快地区房产管理的信息化,结合GIS,MapObjects,SDE技术,统一管理空间数据和属性数据. 相似文献
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基于ArcIMS的福建省探矿权信息服务系统的设计和实现 总被引:1,自引:0,他引:1
福建省探矿权信息服务系统应用ESRI的WEBGIS平台ARCIMS技术,SDE空间数据引擎、ORACLE关系数据库技术及计算机网络技术,实现了探矿权数据库的图文一体化的政务信息公开。本文简要介绍了该系统的设计和实现。 相似文献
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Anderson Chaves Carniel Pedro Vinícius Almeida Borges de Venâncio Markus Schneider 《Transactions in GIS》2023,27(3):900-927
Spatial libraries are core components in many geographic information systems, spatial database systems, and spatial data science projects. These libraries provide the implementation of spatial type systems that include spatial data types and a large diversity of geometric operations. Their focus relies on handling crisp spatial objects, which are characterized by an exact location and a precisely defined extent, shape, and boundary in space. However, there is an increasing interest in analyzing spatial phenomena characterized by fuzzy spatial objects, which have inexact locations, vague boundaries, and/or blurred interiors. Unfortunately, available spatial libraries do not provide support for fuzzy spatial objects. In this article, we describe the R package named fsr, which is based on the Spatial Plateau Algebra and is publicly available at https://cran.r-project.org/package=fsr . Our tool provides methods for building fuzzy spatial objects as spatial plateau objects and conducting exploratory spatial data analysis by using fuzzy spatial operations. 相似文献
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空间数据划分是空间大数据索引方法及其数据存储的重要组成部分。针对Hadoop云计算平台在空间数据划分及其存储方面的不足,提出了基于Hilbert空间填充曲线的海量空间矢量数据并行划分算法。在数据划分阶段,充分考虑空间数据相邻对象的空间位置关系、空间对象的自身大小以及相同编码块的空间对象个数等影响因素;通过“合并小编码块,分解大编码块”的划分原则,实现了云环境下海量空间矢量数据的并行划分算法。试验表明,该算法不仅能够提高海量空间矢量数据的索引效率,同时也能够很好地解决空间矢量数据在Hadoop分布式文件系统(Hadoop distributed file system,HDFS)上的数据倾斜问题。 相似文献
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以多尺度空间数据为研究对象,讨论其混合索引结构的建立方法。对于多尺度矢量空间数据提出基于多层次网格与R 树混合索引结构,栅格数据进行分块并建立四叉树索引结构。利用双向链表结构连接同尺度下的矢栅空间数据,实现矢栅之间的无缝转化;并用地理数据编码及双向链表结构连接不同尺度下的同一空间对象,实现多尺度空间对象的纵向无缝连接。进而实现多尺度空间数据的快速可视化及空间对象的纵向无缝查询。 相似文献
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《制图学和地理信息科学》2013,40(4):317-331
Automation of map generalization requires facilities to monitor the spatial relationships and interactions among multiple map objects. An experimental map generalization system has been developed which addresses this issue by representing spatial objects within a simplicial data structure (SDS) based on constrained Delaunay triangulation of the source data. Geometric generalization operators that have been implemented include object exaggeration, collapse, amalgamation, boundary reduction and displacement. The generalization operators exploit a set of primitive SDS functions to determine topological and proximal relationships, measure map objects, apply transformations, and detect and resolve spatial conflicts. Proximal search functions are used for efficient analysis of the structure and dimensions of the intervening spaces between map objects. Because geometric generalization takes place within a fully triangulated representation of the map surface, the presence of overlap conflicts, resulting from individual operators, can be detected due to the introduction of singularities in the triangulation, the structure of which is used to generate displacement vectors to resolve the conflict. Examples of the application of the implemented operators are described and illustrated using large scale topographic map data. 相似文献
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A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis 总被引:2,自引:0,他引:2
Yu Liu Qinghua Guo Maggi Kelly 《ISPRS Journal of Photogrammetry and Remote Sensing》2008,63(4):461-475
Object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. Object based image classifiers first segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However, less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this “single-valued” space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scale-dependent “line-like objects” and “point-like objects” are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasi- topological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis. 相似文献
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面向图层处理单元的GIS数据模型、数据处理模式已不再适应大规模海量空间数据组织、处理以及网络分发的需要。文章提出以空间要素基元处理取代传统的面向图层处理的模式,并在对象-关系数据库(ORDBMS)统一框架下构筑、组织、存储、处理空间数据,最终形成基于ORDBMS的GIS应用。 相似文献
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空间数据划分是空间数据库系统进行高效空间连接操作的前提和基础。针对现有的空间数据划分方法难以保持低冗余度和高数据量均衡度以及高效支持空间连接的问题,提出了一种基于流形学习的空间数据划分算法。利用流形学习保留降维前源数据结构不变的特点,构建数据划分策略和映射方法,通过将邻近数据划分到同一数据块来减少数据冗余度,通过对最小数据块进行映射,提高整体的数据量均衡度。实验表明,本文提出的划分方法具有极低的数据冗余度和良好的数据量均衡度。 相似文献