Spatial Object Aggregation Based on Data Structure,Local Triangulation and Hierarchical Analyzing Method

This paper focuses on the methods and process of spatial aggregation based on semantic and geometric characteristics of spatial objects and relations among the objects with the help of spatial data structure (Formal Data Structure),the Local Constrained Delaunay Triangulations and semantic hierarchy.The adjacent relation among connected objects and unconnected objects has been studied through constrained triangle as elementary processing unit in aggregation operation.The hierarchical semantic analytical matrix is given for analyzing the similarity between objects types and between objects.Several different cases of aggregation have been presented in this paper.     

多约束条件下示意性道路网地图渐进式综合(英文)

Multiple constraints for schematic road network map cartographic design are analyzed and summarized. Based on this, a set of quantitative criteria are set up and a new road network generalization method including progressive selection and displacement is proposed. Furthermore, topological checking methods for road networks are researched. Based on these constraints, the points in a road network are classified, and a satisfactory and effective schematic map is designed in a con- crete experiment while maintaining topological consistency of the road network between the original and the schematic map     

DCAD： a Dual Clustering Algorithm for Distributed Spatial Databases

Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clus- tering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.     

空间查询中自然语言空间关系与度量空间关系的转换方法研究:以面目标为例

以面目标为例,首先探讨度量空间关系的描述方法,即拓扑谓词+度量参数,建立拓扑谓词与度量参数之间的对应关系;然后从人的认知角度,分析空间关系的自然语言描述,即拓扑谓词+量词,区分模糊量词和临界量词两类量词,并分别用来描述邻近拓扑谓词相互转化过程中的定量变化和定性变化;最后,分析度量参数与量词之间的关联关系,试图建立空间查询中度量空间关系与自然语言空间关系之间的联系,从而更好地应用于空间查询.

Abstract：

Spatial relations between spatial entities can be obtained by two means. One is computed and obtained by spatial data, and the other is obtained by human spatial cognition. So, they are named as metric spatial relations and natural-language spatial relations, respectively. This paper is focused on filling the gap between these two kinds of spatial relations. For this purpose, regions are taken as example, and three steps are included. Metric spatial relations ore first depicted by combining topological predicates with metric parameters, where a mapping is built between the topological predicates and the metric parameters. Second, natural-language spatial relations are represented by combining topological predicates with quantifiers, where quantifiers are differentiated into two types:vague quantifiers and critical quantifiers. They are respectively used to describe quantitative and qualitative change in the process of the transformation between neighboring topological predicates. Finally, the association between metric parameters and quantifiers is further illustrated and analyzed by a questionnaire. With these three steps, the linkage can be built between metric spatial relations and natural-language spatial relations. The proposed methods have been proven to be rational by the performances of spatial query.     

The generic annular bucket histogram for estimating the selectivity of spatial selection and spatial join

Selectivity estimation is crucial for query optimizers choosing an optimal spatial execution plan in a spatial database management system.This paper presents an Annular Bucket spatial histogram(AB histogram)that can estimate the selectivity in finer spatial selection and spatial join operations even when the spatial query has more operators or more joins.The AB histogram is represented as a set of bucket-range,bucket-count value pairs.The bucket-range often covers an annular region like a sin-gle-cell-sized photo frame.The bucket-count is the number of objects whose Minimum Bounding Rectangles(MBRs)fall between outer rectangle and inner rectangle of the bucket-range.Assuming that all MBRs in each a bucket distribute evenly,for every buck-et,we can obtain serial probabilities that satisfy a certain spatial selection or join conditions from the operations’ semantics and the spatial relations between every bucket-range and query ranges.Thus,according to some probability theories,spatial selection or join selectivity can be estimated by the every bucket-count and its probabilities.This paper also shows a way to generate an updated AB histogram from an original AB histogram and those probabilities.Our tests show that the AB histogram not only supports the selectivity estimation of spatial selection or spatial join with "disjoint","intersect","within","contains",and "overlap" operators but also provides an approach to generate a reliable updated histogram whose spatial distribution is close to the distribution of ac-tual query result.     

An optimum vehicular path algorithm for traffic network based on hierarchical spatial reasoning

Human beings' intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms.It is detailed in this paper how to utilize the hierarchical reasoning on the basis of greedy and directional strategy to establish a spatial heuristic,so as to improve running efficiency and suitability of shortest path algorithm for traffic network.The authors divide urban traffic network into three hierarchies and set forward a new node hierarchy division rule to avoid the unreliable solution of shortest path.It is argued that the shortest path,no matter distance shortest or time shortest,is usually not the favorite of drivers in practice.Some factors difficult to expect or quantify influence the drivers' choice greatly.It makes the drivers prefer choosing a less shortest,but more reliable or flexible path to travel on.The presented optimum path algorithm,in addition to the improvement of the running efficiency of shortest path algorithms up to several times,reduces the emergence of those factors,conforms to the intellection characteristic of human beings,and is more easily accepted by drivers.Moreover,it does not require the completeness of networks in the lowest hierarchy and the applicability and fault tolerance of the algorithm have improved.The experiment result shows the advantages of the presented algorithm.The authors argued that the algorithm has great potential application for navigation systems of large-scale traffic networks.     

A Trajectory-Oriented Network Data Model, Carriageway-Based Road Part 3 ： Implementation

This is the final of a three-part series of papers which mainly discusses the implementation issues of the CRNM. The first two papers in the series have introduced the modeling background and methodology, respectively. An overall architecture of the CRNM has been proposed in the last paper. On the basis of the above discusses, a linear reference method （LRM） for providing spatial references for location points of a trajectory is developed. A case study is introduced to illustrate the application of the CRNM for modeling a road network in the real world is given. A comprehensive conclusion is given for the series of papers.     

Mining knowledge from result comparison between spatial clustering themes

This paper introduces some definitions and defines a set of calculating indexes to facilitate the research, and then presents an algorithm to complete the spatial clustering result comparison between different clustering themes. The research shows that some valuable spatial correlation patterns can be further found from the clustering result comparison with multi-themes, based on traditional spatial clustering as the first step. Those patterns can tell us what relations those themes have, and thus will help us have a deeper understanding of the studied spatial entities. An example is also given to demonstrate the principle and process of the method.     

面向对象空间数据模型的结构设计

以面向对象方法提出了空间数据模型,探讨了面向对象空间数据模型中空间对象及类的描述方法和体系结构,分析了体系结构中各层之间的逻辑关系,并以城市道路交通信息为例,设计了道路交通信息的层次划分和关系构成。     

基于交通网络几何和语义(交通)规则集成的公交换乘研究

换乘信息特别是基于地理信息系统技术的图文换乘信息服务,已经成为城市公共交通信息服务、线网规划和线路优化调整的重要指标与内容。但是公共交通线网具有特殊的网络特征和拓扑关系,它不同于城市道路网络,因此公共交通换乘不能简单应用于目前的最优路径算法,例如Dijkstra算法。本文将通过对公共交通线网的空间特征和语义(交通)规则分析,建立城市交通网络的几何和语义连通性的集成关系。几何连通性是语义连通性的基础和先决条件,而语义连通性是公共交通网络拓扑的核心。基于语义连通性的公共交通线网拓扑关系提升公共交通换乘算法的效率和信息准确度,任何两个站点之间的最优路径可以被迅速获取而不需要检索整个网络的几何拓扑信息。并且该最优换乘路径考虑到最少换乘次数、出行距离最短、费用最少、途经站点最少和换乘中最短步行距离等多目标换乘。     

基于语义规则的高分辨率影像道路交叉口提取方法

为了准确提取遥感影像上道路交叉口目标,提出一种利用特征语义规则从高分辨率影像上提取道路交叉口的方法。该算法构建交叉口模型时将其视为由同质区域像素集合及区域轮廓边界构成的面对象,提取过程分为两步:1)利用辐射、纹理特征语义匹配提取交叉口候选区域;2)通过几何特征语义匹配筛选候选区域、识别交叉口属性。利用多源遥感影像对算法正确性及合理性进行验证,结果表明:算法能准确、完整地提取道路交叉口,可为影像道路网构建提供辅助信息。     

地图图形目标之间基本空间关系抽象的规律

以地图自动综合为目的,定义了空间目标的几种基本空间关系.总结了道路、水系、居民地等要素之间存在的典型空间关系,着重讨论了它们之间空间关系抽象的规则,为地图自动综合中有效保持空间关系的一致性提供必要的理论基础.     

道路网信息层次度量方法

针对目前以信息熵度量道路网信息方法存在的不合理、不准确的问题,该文提出一种基于空间认知的道路网信息层次度量方法。从地图空间信息产生的本质出发,结合空间信息认知的层次性特点,将道路网空间信息划分为3个层次:元素层次道路要素的几何形态信息、邻域层次邻接道路的空间关系信息、整体层次道路分布信息。依据各层次空间特征的多样性,分别建立3个层次空间特征的定量描述指标,计算基于各指标的几何形态信息、空间关系信息、道路分布信息。最后,通过实验分析验证了该方法的合理性。     

基于Encoder-Decoder网络的遥感影像道路提取方法

针对道路目标特点,设计实现了用于遥感影像道路提取的Encoder-Decoder深度语义分割网络。首先,针对道路目标局部特征丰富、语义特征较为简单的特点,设计了较浅深度、分辨率较高的Encoder-Decoder网络结构,提高了分割网络的细节表示能力。其次,针对遥感影像中道路目标所占像素比例较小的特点,改进了二分类交叉熵损失函数,解决了网络训练中正负样本严重失衡的问题。在大型道路提取数据集上的试验表明,所提方法召回率、精度和F1-score指标分别达到了83.9%、82.5%及82.9%,能够完整准确地提取遥感影像中的道路目标。所设计的Encoder-Decoder网络性能优良,且不需人工设计提取特征,因而具有良好的应用前景。     

基于道路标线布设方案的交通路网数据库构建研究

道路标线是交通规则的载体,它们布设在物理道路网络上为交通参与者分配路权,并规范其行为。通常,称这种加载了交通规则的物理道路网络为交通路网。本文首先提出了物理道路网络的表示模型;然后结合线性参考技术,以物理道路网络为基础,构建了基于的GIS道路标线布设方案数据模型;最后,本文选取示范路网,运用道路标线布设方案数据库,进行路网交通规则的推导,构建了基于车道级路网数据模型的交通路网数据库,为交通路网数据库的构建提供了一种参考。     

基于几何规则的车载近景立体影像道路交通标线自动提取

道路交通标线信息是道路导航地图中必不可少的数据,对于制作精度更高、道路细节信息更加丰富清晰的高精度导航地图具有重要的作用。本文以车载近景立体影像中的道路交通标线信息自动提取为研究目标,针对道路交通标线快速、精确采集的应用需求,提出了基于几何规则的车载近景立体影像道路交通标线自动提取方法。该方法首先分析了道路交通标线的几何特征;其次,构建了基于几何特征的交通标线信息提取规则;最后,以标线种子点为基础,结合前述规则,实现了道路交通标线三维空间信息的自动采集。以南京师范大学的车载移动测量系统拍摄的实际近景立体影像为数据进行了试验,试验结果证明,本文方法相比传统人机交互采集方法,在交通标线特征点采集效率、标线几何相似度等方面有较大的优势,可为高精度导航地图的生产提供一种可借鉴的技术支撑。     

基于GIS的青岛市中心城区道路网络可达性研究

针对青岛市中心城区交通网络建设不完善的问题,研究了该区域路网的综合可达性,并提出了相应的解决方案。基于GIS平台,建立了青岛市中心城区路网模型;选取最小阻抗(时间和距离)、交叉口服务指数、路网密度和路网连通度作为关键评价指标,分别从路网距离关系、路网结构合理性、路网密度和路网发育程度4个方面分析了该区域的路网可达性;在此基础上,建立了青岛市中心城区路网综合评价模型。研究结果表明,该区域的路网以多中心多圈层形式分布,整体可达性良好;西南区域路网密度低,南部外围区域机动性差,南部地区可达性较差,应不断加强该地区的基础设施与路网建设。