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地图综合的本质是一种空间相似变换,制图者在相似原则的指导下实施概括,读图者从包含相似性的地图中形成心象地图、重构现实世界。因此,多尺度地图空间中的相似关系研究非常重要。然而,由于相似的可计算性差,且其计算的目的在于揭示更深层次的信息,地图综合中相似关系尤其是语义相似关系的研究相对较少。针对这一问题,本文以语义功能区约束下的大比例尺街区式居民地合并(1∶1750至1∶4000)为例,基于匹配距离模型计算建筑物合并中的语义相似度,得到语义相似度在关键比例尺节点的值,并对结果进行分析、评价。试验表明,语义功能区约束下的建筑物合并符合读图者的地图认知需求,本文所述方法有助于地图更好地发挥信息传输载体的作用。 相似文献
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如何快速、自动地实现多尺度地图自动综合结果质量评价,对提高空间数据质量、加快空间数据生产周期等具有重要意义.本文在综合考虑拓扑关系、方向关系和距离关系的基础上,基于SRM模型提出了基于面状目标的空间关系相似性的度量方法,为地图自动综合提供空间关系评价和维护的参考. 相似文献
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首先以居民地要素为例,通过对多对多关系产生原因的分析,明确制图综合中典型化操作是多对多关系产生的主要根源。其次,从对多对多匹配的实例入手,根据邻近、轮廓规则、分布有序等特征,设计了多对多关系的探测和发现方法。最后,根据典型化算法整体性、结构化的原则,从群对象的对象结构、轮廓形状、面积和方位等特征入手,设计了相邻比例尺多对多匹配算法。实验表明,该方法符合人类的匹配认知习惯,能够有效发现并确认相邻比例尺中同名对象之间的多对多对应关系。 相似文献
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用专家系统技术实施居民地自动综合 总被引:2,自引:0,他引:2
本文主要论述了利用现有地图数据库数据,运用专家系统中产生式规则表示知识的方法,对居民地要素进行自动综合,生成小比例尺地图上居民地数据的方法。 相似文献
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通过对数学形态学相关算法的研究分析,结合地图上居民地街区合并的特点,提出了用数学形态学中图像闭运算的算法进行居民地街区自动合并的方法,为地图上街区式居民地的合并综合,提供了一种新的方法。 相似文献
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多尺度点群相似度计算在制图综合过程控制及结果评价中具有重要作用。针对现有方法的不足,提出一种基于广义Hausdorff距离的多尺度点群相似度计算方法。在传统Hausdorff距离基础上,建立距离相似度计算公式;给出拓扑距离的定义及计算方法,建立基于拓扑Hausdorff距离的拓扑相似度计算公式;以点群最小外包圆为基础建立方向关系参考框架,给出方向距离定义,建立基于方向Hausdorff距离的方向相似度计算公式,并得出总相似度计算公式。通过多尺度点群相似度计算实验及综合结果评价实验,验证了所述方法的可行性和有效性。 相似文献
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On the spatial distribution of buildings for map generalization 总被引:1,自引:0,他引:1
Information on spatial distribution of buildings must be explored as part of the process of map generalization. A new approach is proposed in this article, which combines building classification and clustering to enable the detection of class differences within a pattern, as well as patterns within a class. To do this, an analysis of existing parameters describing building characteristics is performed via principal component analysis (PCA), and four major parameters (i.e. convex hull area, IPQ compactness, number of edges, and smallest minimum bounding rectangle orientation) are selected for further classification based on similarities between building characteristics. A building clustering method based on minimum spanning tree (MST) considering rivers and roads is then applied. Theory and experiments show that use of a relative neighbor graph (RNG) is more effective in detecting linear building patterns than either a nearest neighbor graph (NNG), an MST, or a Gabriel graph (GssG). Building classification and clustering are therefore conducted separately using experimental data extracted from OpenStreetMap (OSM), and linear patterns are then recognized within resultant clusters. Experimental results show that the approach proposed in this article is both reasonable and efficient for mining information on the spatial distribution of buildings for map generalization. 相似文献
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Automated reconstruction of building objects from aerial images is a complex problem due to the diversity of buildings as well as noise and low contrast of images, which are the results of distant photography, atmospheric effects and poor illumination. In this paper, a semi-automated approach to the reconstruction of parametric building models from aerial images is presented, which works with line segments extracted from the image. The model is selected interactively from a library of parametric models. A perceptual grouping technique is used to select the most significant image lines in terms of relations such as proximity and parallelism. Model lines are searched for the same relations as in the grouped image lines, and the corresponding lines undergo a matching procedure, which determines whether or not a match can be found between the given model and image lines. An experiment with aerial images of flat-roof and gable-roof buildings is shown and its results indicate the robustness and efficiency of the proposed approach. 相似文献
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In map generalization, displacement is the most frequently used operator to reduce the proximity conflicts caused by reducing scales or other generalization operations. Building displacement can be formalized as a combinatorial optimization problem, and a heuristic or intelligent search algorithm can be borrowed to obtain the solution. In this way, we can explicitly resolve minimum distance conflicts and control positional accuracy during the displacement. However, maintaining spatial relations and patterns of buildings can be challenging. To address spatial conflicts as well as preserve the significant spatial relations and patterns of buildings, we propose a new spatial contextual displacement algorithm based on an immune genetic algorithm. To preserve important spatial relations and global patterns of map objects and avoid topology errors, displacement safety zones are constructed by overlapping the Voronoi tessellation and buffer areas of the buildings. Additionally, a strategy to shift the buildings in a building group synchronously is used to maintain local building patterns. To demonstrate the effectiveness of our algorithm, two data sets with different building densities were tested. The results indicate that the new algorithm has obvious advantages in preventing topology errors and preserving spatial relations and patterns. 相似文献
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制图综合中建筑物多边形的合并与化简 总被引:2,自引:0,他引:2
讨论了数字环境下顾及建筑物矩形几何特征的多边形自动综合算法,针对多边形之间的拓扑邻近与视觉邻近两种空间关系,提出了基于矢量和基于栅格的两种建筑物多边形合并方法。关于建筑物形状的化简,本文提出了矩形差分方法,并在此基础上建立了建筑物多边形化简的层次化途径。 相似文献
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地图综合中,建筑物群的排列结构是需要重点考虑的因素。当不同排列的子建筑物群之间存在空间图形冲突时,这些建筑物群的综合就显得更为复杂。直线排列建筑物群的综合在大比例尺地形图上以典型化操作为主。本文提出一种相互之间存在潜在空间图形冲突的多个直线排列建筑物子群的渐进式典型化方法,渐进式地处理多个直线排列建筑物子群之间的空间图形冲突,保留建筑物群重要的直线排列结构;以建筑物表达的视觉图形约束为限制条件,自动确定典型化后的建筑物位置、形状、大小和方位。本文还研究了基于建筑物群空间邻近图的直线排列建筑物子群的自动识别方法,分析了这些直线排列之间的邻近关系和相交关系。最后,以1:5000地图上的建筑物群综合为1:25 000为试验对象,验证了所提出算法的可用性和有效性。 相似文献
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房顶是三维建筑的重要组成部分,房顶结构的识别是三维建筑综合算法实现和进行城市建筑空间分布模式分析的基础。分析了建筑房顶结构特点,提出了一种三维建筑分类的实用方法,针对不同房顶类型设计了相应的结构化表达方法,在此基础上提出了三维建筑房顶面信息提取算法,并进一步实现了房顶类型的识别。实验证明,该方法识别的准确性较高,能够满足进一步应用的需求。 相似文献
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Proximity-based grouping of buildings in urban blocks: a comparison of four algorithms 总被引:4,自引:0,他引:4
Grouping of buildings based on proximity is a pre-processing step of urban pattern (structure) recognition for contextual cartographic generalization. This paper presents a comparison of grouping algorithms for polygonal buildings in urban blocks. Four clustering algorithms, Minimum Spanning Tree (MST), Density-Based Spatial Clustering Application with Noise (DBSCAN), CHAMELEON and Adaptive Spatial Clustering based on Delaunay Triangulation (ASCDT) are reviewed and analysed to detect building groups. The success of the algorithms is evaluated based on group distribution characteristics (i.e. distribution of the buildings in groups) with two methods: S_Dbw and newly proposed Cluster Assessment Circles. A proximity matrix of the nearest distances between the building polygons, and Delaunay triangulation of building vertices are created as an input for the algorithms. A topographic data-set at 1:25,000 scale is used for the experiments. Urban block polygons are created to constrain the clustering processes from topological aspect. Findings of the experiment demonstrate that DBSCAN and ASCDT are superior to CHAMELEON and MST. Among them, MST has exhibited the worst performance for finding meaningful building groups in urban blocks. 相似文献
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本文从空间-语义双重约束角度,提出一种顾及空间邻近和功能语义相似的建筑物空间分布模式识别方法。首先,基于建筑物的空间位置邻近性(即建筑物间的最小距离)约束进行聚类,获得建筑物的空间分布模式和建筑物间的空间邻近关系;然后,根据建筑物的功能语义相似性约束进行分割,获得建筑物的初步聚类结果;最后,考虑簇内相似性与簇间差异性进行整体优化,获得最终聚类结果。试验验证表明,本文方法比现有方法能够更有效地识别空间邻近与功能语义一致的建筑物群,服务于智慧城市建设中对建筑物进行语义层次综合和对城市结构进行深入研究的需求。 相似文献