Constrained tGAP for generalization between scales: The case of Dutch topographic data

This article presents the results of integrating large- and medium-scale data into a unified data structure. This structure can be used as a single non-redundant representation for the input data, which can be queried at any arbitrary scale between the source scales. The solution is based on the constrained topological Generalized Area Partition (tGAP), which stores the results of a generalization process applied to the large-scale dataset, and is controlled by the objects of the medium-scale dataset, which act as constraints on the large-scale objects. The result contains the accurate geometry of the large-scale objects enriched with the generalization knowledge of the medium-scale data, stored as references in the constraint tGAP structure. The advantage of this constrained approach over the original tGAP is the higher quality of the aggregated maps. The idea was implemented with real topographic datasets from The Netherlands for the large- (1:1000) and medium-scale (1:10,000) data. The approach is expected to be equally valid for any categorical map and for other scales as well.     

LOCALIS: Locally-adaptive Line Simplification for GPU-based Geographic Vector Data Visualization

Visualization of large vector line data is a core task in geographic and cartographic systems. Vector maps are often displayed at different cartographic generalization levels, traditionally by using several discrete levels-of-detail (LODs). This limits the generalization levels to a fixed and predefined set of LODs, and generally does not support smooth LOD transitions. However, fast GPUs and novel line rendering techniques can be exploited to integrate dynamic vector map LOD management into GPU-based algorithms for locally-adaptive line simplification and real-time rendering. We propose a new technique that interactively visualizes large line vector datasets at variable LODs. It is based on the Douglas-Peucker line simplification principle, generating an exhaustive set of line segments whose specific subsets represent the lines at any variable LOD. At run time, an appropriate and view-dependent error metric supports screen-space adaptive LOD levels and the display of the correct subset of line segments accordingly. Our implementation shows that we can simplify and display large line datasets interactively. We can successfully apply line style patterns, dynamic LOD selection lenses, and anti-aliasing techniques to our line rendering.     

基于注意力机制的多尺度融合航拍影像语义分割

航拍影像同一场景不同对象尺度差异较大,采用单一尺度的分割往往无法达到最 佳的分类效果。为解决这一问题,提出一种基于注意力机制的多尺度融合模型。首先,利用不 同采样率的扩张卷积提取航拍影像的多个尺度特征;然后,在多尺度融合阶段引入注意力机制, 使模型能够自动聚焦于合适的尺度,并为所有尺度及每个位置像素分别赋予权重;最后,将加 权融合后的特征图上采样到原图大小,对航拍影像的每个像素进行语义标注。实验结果表明, 与传统的 FCN、DeepLab 语义分割模型及其他航拍影像分割模型相比,基于注意力机制的多尺 度融合模型不仅具有更高的分割精度,而且可以通过对各尺度特征对应权重图的可视化,分析 不同尺度及位置像素的重要性。     

A functional perspective on map generalisation

In the context of map generalisation, the ambition is to store once and then maintain a very detailed geographic database. Using a mix of modelling and cartographic generalisation techniques, the intention is to derive map products at varying levels of detail – from the fine scale to the highly synoptic. We argue that in modelling this process, it is highly advantageous to take a ‘functional perspective’ on map generalisation – rather than a geometric one. In other words to model the function as it manifests itself in the shapes and patterns of distribution of the phenomena being mapped – whether it be hospitals, airports, or cities. By modelling the functional composition of such features we can create relationships (partonomic, taxonomic and topological) that lend themselves directly to modelling, to analysis and most importantly to the process of generalisation. Borrowing from ideas in robotic vision this paper presents an approach for the automatic identification of functional sites (a collection of topographic features that perform a collective function) and demonstrates their utility in multi-scale representation and generalisation.     

多尺度粗糙表面的实时绘制方法

目的 大部分材质表面都具有一定的细微结构,而这些细微结构的存在增加了真实感图形绘制的复杂性.方法 首先将材质表面的细微结构分为3类:宏观结构、介观结构和微观结构,并对每类结构分别建模:宏观结构采用三角面片建模,介观结构则采用法向图表示,而微观结构直接采用单一的粗糙度表达.然后针对每种结构,分别获得它们的法向分布函数(NDF),并用混合vMF分布拟合.最终屏幕空间每个像素内的法向分布用3个尺度NDF的卷积操作近似获得.此外,在处理环境光照时引入抛物面图(PM)和summed-area table(SAT),满足了动态场景的实时绘制需求.结果 实验结果表明本文方法可以在不同视点范围下生成高真实感的反射效果,并获得实时的绘制性能.结论 本文提出的实时绘制方法能够处理表面材质结构复杂的3维模型在环境光下的真实感反射效果,并支持动态光照和形变物体.     

路网要素多尺度表达目标的层次关系分析

针对目前地理要素多尺度建模的热点问题,剖析构建多尺度空间数据模型的关键技术,以路网要素为例,挖掘地理要素多尺度抽象表达的机理,利用多尺度扩展E-R方法,提炼多尺度抽象表达目标间的各种尺度、层次、语义、映射等层次语义关系,实现GIS中地理要素多尺度概念模型的设计。     

Evaluating structural and topological consistency of complex regions with broad boundaries in multi-resolution spatial databases

Multi-resolution or multi-scale spatial databases store and manage multiple representations of spatial objects in the same area, so consistency among multiple representations of the same objects should be evaluated and maintained. Although many approaches have been proposed to check inconsistencies in multi-resolution databases, there is still a lack of effective approaches working for complex objects, especially for regions with broad boundaries which is a general model for representing various types of uncertainties. This paper presents approaches for evaluating structural and topological consistency among multiple representations of complex regions with broad boundaries (CBBRs) based on map generalization operators: merging, dropping, and hybrid of these two. For evaluation of structural consistency, all possible multiple representations of a CBBR are generated automatically and organized into a structured neighborhood graph, and then correspondences and equivalences among the multiple representations are defined to determine whether two representations at different levels of detail are structurally consistent. For evaluation of topological consistency, the topological relations between all pairs of regions in two CBBRs are considered, and their variation with change of spatial scale is analyzed. Since the approaches in this paper are built on a hiearchical representation of CBBRs with arbitrarily complex structure, they will also work well for evaluating consistency among multiple representations of complex objects.     

Synchronous collaborative tunnel design based on consistency-preserving multi-scale models

The planning of large infrastructure projects such as inner-city subway tracks is a highly collaborative process in which numerous experts from different domains are involved. While performing the planning task, widely differing scales have to be taken into consideration, ranging from the kilometer scale for the general routing of the track down to the centimeter scale for the detailed design of connection points. Currently there is no technology available which supports both the collaborative as well as the multi-scale aspect in an adequate manner. To fill this technological gap and better support the collaborative design and engineering activities involved with infrastructure planning, this paper introduces a new methodology which allows engineers to simultaneously manipulate a shared multi-scale tunnel model. This methodology comprises two main aspects. The first aspect is a multi-scale model for shield tunnels, which provides five different levels of detail (LoD) representing the different levels of abstraction required throughout the planning progress. The second aspect is a conceived collaboration platform, which enables simultaneous modifications of the multi-scale model by multiple users. In existing multi-scale approaches, where the individual representations are stored independently from each other, there is a high risk of creating inconsistencies, in particular in the highly dynamic collaborative planning context. To overcome this issue, the concept presented in this paper makes use of procedural modeling techniques for creating explicit dependencies between the geometric entities on the different LoDs. This results in a highly flexible, yet inherently consistent multi-scale model where the manipulation of elements on coarser LoDs results in an automated update of all dependent elements on finer LoDs. The proposed multi-scale model forms a well-suited basis for realizing the collaboration concept, which allows several experts to simultaneously manipulate a shared infrastructure model on various scales while using the different design tools they are accustomed to. The paper discusses in detail the principles and advantages of the proposed multi-scale modeling approach as well as its application in the context of collaborative tunnel design. The paper concludes with a case study of a large infrastructure project: a new inner-city subway tunnel in Munich, Germany.     

基于C—Tree的无级比例尺GIS多边形综合技术

无级比例尺GIS是数字地球和Web GIS的核心技术之一,但随着GIS的广泛应用和深入发展,现有的GIS技术已经不能满足信息社会的需要,其中一个需要解决的重要问题就是GIS的空间数据量如何随着比例尺的变化自动增减。针对无级比例尺GIS多边形综合中的选取与合并技术,在对选取的数量规律和质量原则以及合并的原则进行充分论述的基础上,提出了一种多边形图层数据组织策略C-Tree,并给出了基于C-Tree的多边形综合算法。对于给定的大给与大比例尺地图多边形图层数据,该算法可以高效率地完成多边形选取与合并的综合操作,输出小比例尺地图图层数据。该算法现已成功应用于时空一体化智能城建信息系统,并获得了满意的结果。     

Local optimal scale in a hierarchical segmentation method for satellite images

Over recent decades, remote sensing has emerged as an effective tool for improving agriculture productivity. In particular, many works have dealt with the problem of identifying characteristics or phenomena of crops and orchards on different scales using remote sensed images. Since the natural processes are scale dependent and most of them are hierarchically structured, the determination of optimal study scales is mandatory in understanding these processes and their interactions. The concept of multi-scale/multi-resolution inherent to OBIA methodologies allows the scale problem to be dealt with. But for that multi-scale and hierarchical segmentation algorithms are required. The question that remains unsolved is to determine the suitable scale segmentation that allows different objects and phenomena to be characterized in a single image. In this work, an adaptation of the Simple Linear Iterative Clustering (SLIC) algorithm to perform a multi-scale hierarchical segmentation of satellite images is proposed. The selection of the optimal multi-scale segmentation for different regions of the image is carried out by evaluating the intra-variability and inter-heterogeneity of the regions obtained on each scale with respect to the parent-regions defined by the coarsest scale. To achieve this goal, an objective function, that combines weighted variance and the global Moran index, has been used. Two different kinds of experiment have been carried out, generating the number of regions on each scale through linear and dyadic approaches. This methodology has allowed, on the one hand, the detection of objects on different scales and, on the other hand, to represent them all in a single image. Altogether, the procedure provides the user with a better comprehension of the land cover, the objects on it and the phenomena occurring.     

基于改进的约束四叉树LOD全球地形实时绘制

由于大规模三维地形可视化的数据量大、组织结构复杂等特点,对大规模地形数据的分块、调度和组织已成为研究重点。传统的INY．树结构存在节点重复储存、大地形绘制效率不高、T形裂缝等问题,论文提出了一种改进的约束四叉树多细节层次绘制算法,减少了储存冗余。在全球地形实时绘制的时候,采用了改进的细节层次细分评价函数和简单的“裙边”裂缝处理方法,并且使用了四种绘制优化策略。通过实验结果可以看出,计算机储存减少,计算量降低,大地形的实时绘制效果、效率很好。对虚拟城市、数字地球的构建和应用有参考价值。     

Conflict Reduction in Map Generalization Using Iterative Improvement

Map data are usually derived from a source that is based on a particular scale of representation and hence are subject to a particular degree of map generalization. Attempts to display data at scales smaller than the source can result in spatial conflict, whereby map symbols become too close or overlap. Several map generalization operators may be applied to resolve the problem, including displacement. In this paper we address the problem of displacing multiple map objects in order to resolve graphic conflict. Each of n objects is assigned k candidate positions into which it can possibly move, resulting in a total of kⁿ map realizations. The assumption is that some of these realizations will contain a reduced level of conflict. Generating and evaluating all realizations is however not practical, even for relatively small values of n and k. We present two iterative improvement algorithms, which limit the number of realizations processed. The first algorithm adopts a steepest gradient descent approach; the second uses simulated annealing. They are tested on a number of data sets and while both are successful in reducing conflict while limiting the number of realizations that are examined, the simulated annealing approach is superior with regard to the degree of conflict reduction. The approach adopted is regarded as generic, in the context of map generalization, in that it appears possible in principle to employ several map generalization operators combined with more sophisticated evaluation functions.     

基于多尺度等值面设计传递函数

在体绘制中传递函数将体数据转换成光学参数,因此体绘制的效果直接由传递函数决定.本文提出了基于多尺度等值面设计传递函数的高效方法.该方法通过梯度阈值提取边界体元来简化数据场,然后将提取等值面的目标函数的计算化简为累加的拉普拉斯加权的直方图极值的计算.最后对直方图进行多尺度平滑,利用提取出的多尺度等值面来设计高斯型传递函数,提高了等值面的准确度和传递函数的设计效率.     

广义多尺度决策系统中基于可变精度的最优尺度组合

针对广义多尺度决策系统的知识表示与知识获取问题,讨论广义多尺度决策系统中基于对偶概率粗糙集模型的最优尺度组合选择问题.定义广义多尺度决策系统中β下近似最优尺度组合、 β上近似最优尺度组合、 β信任分布最优尺度组合与β似然分布最优尺度组合概念,分析不同最优尺度组合之间的关系与特征,证明当β在特殊的阈值范围内时, β下近似最优尺度组合与最大分布最优尺度组合等价,而β上近似最优尺度组合与广义决策最优尺度组合等价.     

Density-based multi-scale flow mapping and generalization

Mapping large volume of origin-destination flow data (or spatial interactions) has long been a challenging problem because of the conflict between massive location-to-location connections and the limited map space. Current approaches for flow mapping only work with a small dataset or have to use data aggregation, which not only cause a significant loss of information but may also produce misleading maps. In this paper, we present a density-based flow map generalization approach that can extract flow patterns and facilitate the analysis and visualization of big origin-destination flow data at multiple scales. Unlike existing methods that generalize flow data by spatial unit-based aggregation, our new flow map generalization algorithm is based on flow density distribution. To demonstrate the approach and assess its effectiveness, a case study is carried out to map 829,039 taxi trips within the New York City. With parameter settings, the proposed method can discover inherent and abstract flow patterns at different map scales and generalization levels, which naturally supports interactive and multi-scale flow mapping.     

Third-order generalization: A new approach to categorizing higher-order generalization

Generalization, in its most basic form, is an artificial neural network's (ANN's) ability to automatically classify data that were not seen during training. This paper presents a framework in which generalization in ANNs is quantified and different types of generalization are viewed as orders. The ordering of generalization is a means of categorizing different behaviours. These orders enable generalization to be evaluated in a detailed and systematic way. The approach used is based on existing definitions which are augmented in this paper. The generalization framework is a hierarchy of categories which directly aligns an ANN's ability to perform table look-up, interpolation, extrapolation, and hyper-extrapolation tasks.

The framework is empirically validated. Validation is undertaken with three different types of regression task: (1) a one-to-one (o–o) task, f(x):x_i→y_j; (2) the second, in its f(x):{x_i,x_i+1, …}→y_j formulation, maps a many-to-one (m–o) task; and (3) the third f(x):x_i→{y_j,y_j+1, …} a one-to-many (o–m) task. The first and second are assigned to feedforward nets, while the third, due to its complexity, is assigned to a recurrent neural net.

Throughout the empirical work, higher-order generalization is validated with reference to the ability of a net to perform symmetrically related or isomorphic functions generated using symmetric transformations (STs) of a net's weights. The transformed weights of a base net (BN) are inherited by a derived net (DN). The inheritance is viewed as the reuse of information. The overall framework is also considered in the light of alignment to neural models; for example, which order (or level) of generalization can be performed by which specific type of neuron model.

The complete framework may not be applicable to all neural models; in fact, some orders may be special cases which apply only to specific neuron models. This is, indeed, shown to be the case. Lower-order generalization is viewed as a general case and is applicable to all neuron models, whereas higher-order generalization is a particular or special case. This paper focuses on initial results; some of the aims have been demonstrated and amplified through the experimental work.     

使用多重纹理实时绘制Phong高光表面

物体表面的高光现象均由镜面反射的一个复杂的非线性表达式描述,这为实时绘制带来了困难,由此本文提出了一种使用纹理映射技术绘制高光表面的方法。首先将具有镜面反射表达式的Phong模型分解成多个预计算的函数,然后存储为纹理图,最后使用多重纹理技术将这些纹理组合起来,实现高光表面的绘制。实验结果表明,本文算法大大提高了绘制的性能。     

Enhancing the experience of 3D virtual worlds with a cartographic generalization approach

In this work we propose a new approach for fast visualization and exploration of virtual worlds based on the use of cartographic concepts and techniques. Versions of cartographic maps with different levels of details can be created by using a set of operations named cartographic generalization. Cartographic generalization employs twelve operators and domain-specific knowledge, being the contribution of this work their transposition to 3D virtual worlds. The architecture of a system for 3D generalization is proposed and the system is implemented. Differently from traditional cartographic processes, we use artificial intelligence for both selecting the key objects and applying the operators. As a case study, we present the simplification of the historical quarter of Recife (Brazil).     

一种用于空间数据多尺度表达的R树索引结构

针对现有R树无法支持空间数据多尺度表达的问题,提出了一种用于空间数据多尺度表达的R树变形索引结构:(1)允许空间对象出现在非叶结点上;(2)利用树的深度反映空间分辨率的变化,提供分辨率维的支持;(3)树的分支结构考虑对自动制图综合算法的支持.分析了该变形R树索引结构的空间数据多尺度查询过程,并着重对该索引结构生成算法中的约束条件、插入算法和分裂算法进行了讨论.针对相同数据源,使用该方法与基于四叉树的空间数据多尺度索引方法进行了对比实验,结果表明,该索引方法能有效检索多分辨率形式组织的空间数据,具有综合结果记忆功能,效率明显.