A spatio-temporal ontology for geographic information integration

This paper presents an axiomatic formalization of a theory of top-level relations between three categories of entities: individuals, universals, and collections. We deal with a variety of relations between entities in these categories, including the sub-universal relation among universals and the parthood relation among individuals, as well as cross-categorial relations such as instantiation and membership. We show that an adequate understanding of the formal properties of such relations – in particular their behavior with respect to time – is critical for geographic information processing.

The axiomatic theory is developed using Isabelle, a computational system for implementing logical formalisms. All proofs are computer verified and the computational representation of the theory is available online.     

Application of evidential reasoning to improve the mapping of regenerating forest stands

This study confirmed the ability of the Dempster–Shafer theory (DST) and the Dezert–Smarandache (Free DSm model) theory to significantly improve the quality of maps of regenerating forest stands in southern Quebec, Canada compared to a classical Maximum Likelihood Algorithm (MLA). The proposed approach uses data fusion methods that allow the integration of remotely sensed imagery with conventional maps of ecophysiographic features. While the MLA provided an overall accuracy of 82.75%, the DST and Free DSm models had overall accuracies of 90.14% and 91.13% respectively. In addition, this study showed that the data fusion methods can model the influence of biophysical parameters (e.g., surface deposits and drainage) on the growth potential of regenerating forest stands. This study illustrates the importance of the mass function allocation for each ancillary data source. We found that a Bayesian belief configuration provided results equivalent to those obtained when representing data uncertainty. This demonstrates the difficulty in modelling uncertainty associated with each ancillary source.     

Automatic reasoning for geometric constraints in 3D city models with uncertain observations

This paper presents a novel approach to automated geometric reasoning for 3D building models. Geometric constraints like orthogonality or parallelity play a prominent role in man-made objects such as buildings. Thus, constraint based modelling, that specifies buildings by their individual components and the constraints between them, is a common approach in 3D city models. Since prototyped building models allow one to incorporate a priori knowledge they support the 3D reconstruction of buildings from point clouds and allow the construction of virtual cities. However, high level building models have a high degree of complexity and consequently are not easily manageable. Interactive tools are needed which facilitate the development of consistent models that, for instance, do not entail internal logical contradictions. Furthermore, there is often an interest in a compact, redundancy-free representation. We propose an approach that uses algebraic methods to prove that a constraint is deducible from a set of premises. While automated reasoning in 2D models is practical, a substantial increase in complexity can be observed in the transition to the three-dimensional space. Apart from that, algebraic theorem provers are restricted to crisp constraints so far. Thus, they are unable to handle quality issues, which are, however, an important aspect of GIS data and models. In this article we present an approach to automatic 3D reasoning which explicitly addresses uncertainty. Hereby, our aim is to support the interactive modelling of 3D city models and the automatic reconstruction of buildings. Geometric constraints are represented by multivariate polynomials whereas algebraic reasoning is based on Wu’s method of pseudodivision and characteristic sets. The reasoning process is further supported by logical inference rules. In order to cope with uncertainty and to address quality issues the reasoner integrates uncertain projective geometry and statistical hypothesis tests. Consequently, it allows one to derive uncertain conclusions from uncertain premises. The quality of such conclusions is quantified in a way which is sound both from a logical and a statistical perspective.     

DSM辅助下城区大比例尺正射影像镶嵌线智能检测

传统算法以重叠区域色彩(或灰度)差值图像为正射影像镶嵌线检测基础,可以很好地避开色差区域,但对建筑物高密集的城区效果不明显。本文以高精度数字表面模型为检测基础,通过智能识别处理手段对地面区域与非地面区域进行良好区分。传统镶嵌线检测算法都是以迭代运算为基础的智能算法,算法复杂度高。本文发展出一种贪婪蛇型算法,该算法仅与搜索步距、方向旋转间隔、以及重叠区域宽度等三个因素相关,具有速度快,效率高等优点。针对典型的城市区域进行镶嵌线检测实验,结果表明DSM辅助下的城区正射影像镶嵌线能很好地避开房屋等明显地表实体,并且贪婪蛇型算法能有效地进行最优路径检测。     

不确定区域间拓扑关系定性推理

提出不确定区域间拓扑关系的组合描述法,它对拓扑关系的描述能力与扩展九交完全相同.但组合描述模型的优点在于用已有的5种面/面基本拓扑关系的组合来表示44种不确定区域间的复杂拓扑关系,可以把44种复杂拓扑关系的推理转化为5种基本拓扑关系推理的组合,从而实现不确定区域间拓扑关系的推理.     

基于案例推理的历史登陆战役地理环境分析

基于案例推理是智能决策辅助系统中的一种重要技术。本文结合历史登陆战役地理环境,对登陆战役所涉及的各种地理要素进行了分析,把登陆地域所涉及的领域背景知识表示成概念层次树形式,使数据的分布模式具有更强的可视性。将案例推理技术引用于历史登陆战役地理环境查询分析系统,研究案例库的结构设计、案例的组织和表示、地理环境处置规则的表示等。叙述了案例库的建立方法、属性权重算法和相似性度量算法,以及系统的结构和实现方法。     

基于Delaunay三角网的空间邻近关系推理

空间邻近关系是一种重要的空间关系,对这种关系的识别是任何空间建模系统所必须的。Delaunay三角网是根据最小角最大规则建立的一种三角剖分,可以较好地表达空间目标之间的邻近关系。利用Delaunay三角网对空间邻近关系进行了描述,在此基础上提出了空间邻近关系推理方法。     

ID3决策树推理模型及其在道路网选取中的应用

介绍了自动制图综合中知识推理的研究现状及存在的问题,指出了借鉴人工智能优秀技术成果提升自动综合智能化水平是解决当前问题的重要途径之一。对ID3智能算法的基本原理进行了详细介绍,提出基于ID3决策树的知识推理模型。将该模型引入到道路网智能化选取当中。对该模型应用到道路网自动选取的合理性进行了论述,并进行了实验验证。在对实验结果进行详细分析的基础上,探讨了该算法应用于道路网选取的优势和不足,指出了进一步研究的方向。     

Computational complexity of spatial reasoning with directional relationship

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