城市地图数据合并的平差方法

地图数字合并是指在同名实体匹配的基础上,调整相关地物实体的几何位置,以实现同一地区不同来源地图数据库的集成和信息融合.城市地图数据有其自身独有的特点,因此在对城市地图数据库进行合并时,不仅要消除不同图形空间数据之间的差异,还需要精确保持实体原有形状.本文提出了一种基于最小二乘平差的合并算法,采用附带条件的间接平差方法,...     

地图制图学(地图学)

CH20072627基于概率的地图实体匹配方法=A Proba-bilistic Theory-based Matching Method/童小华(同济大学测量与国土信息工程系),史文中(香港理工大学土地测量与地理资讯学系)∥测绘学报.-2007,36(2).-210~217数字地图合并是通过同名实体匹配和合并变换技术,调整相关地物实体的几何、属性等差异,实现同一地区不同来源地图数据的集成和融合。其中同名实体匹配是极为重要的第一步,也是一个存在大量不确定性的过程,匹配阈值的选取、实体非一对一的匹配关系是匹配中的关键难题,匹配效果不佳或出现错误匹配直接影响着后续合并结果的正确性。…     

基于概率的地图实体匹配方法

数字地图合并是通过同名实体匹配和合并变换技术,调整相关地物实体的几何、属性等差异,实现同一地区不同来源地图数据的集成和融合。其中同名实体匹配是极为重要的第一步,也是一个存在大量不确定性的过程,匹配阈值的选取、实体非一对一的匹配关系是匹配中的关键难题,匹配效果不佳或出现错误匹配直接影响着后续合并结果的正确性。本文提出一种基于概率理论的匹配模型,该模型融合多种匹配指标,通过计算实体匹配概率大小来确定匹配实体。该方法避免了匹配指标精确阈值的选取,并且能够有效地解决匹配中非一对一的情况。     

城市地图数据库面实体匹配技术

同一地区不同来源地图数据库同名面实体的识别或匹配对空间数据库的集成与信息共享非常有意义。该文研究了城市地图数据库同名面实体匹配的有关问题 ,提出了基于模糊拓扑关系分类的面实体匹配方法 ,该方法充分考虑了源地图数据库的不确定性 ,能处理非一对一的匹配情况。匹配结果不仅可以作为城市地图数据库集成和信息共享的依据 ,还可以用来分析两个地图数据库之间的差异 ,进行变化检测与自动更新     

核密度估计的道路匹配方法探讨

针对目前地图合并技术的同名实体匹配中线实体(以道路为例)研究的不足,文章基于密度插值和DEM分析,提出一种针对线实体进行全局匹配的方法:采用积分的思想将核密度估计方法扩展应用于线实体密度计算,通过提取其密度谷脊线,将其应用于地图合并技术领域中的线实体匹配研究主题,指导相似度指标阈值的选取,并取得了较好的效果,为实现同一地区不同来源或不同比例尺(主要是相近比例尺)地图数据合并及数据库集成提供了参考。     

特征驱动下区实体匹配指标自适应融合技术

空间实体匹配过程中多个指标的融合问题是影响匹配效果的关键问题之一。本文针对这一问题,以区实体为例提出了一套基于范例库的解决方案。首先提取出影响实体匹配的数据特征因子并确定了量化方法,其次选取典型的匹配指标,接下来通过建立指标权值范例库确定各指标权值,最后根据权值和数据特征因子调整匹配过程。该方法使得数据具有学习能力,达到了指标权值的自适应性的目标。实验表明该方法可行,并且可以提升空间实体匹配算法的效率、准确度和智能化程度。     

利用空白区域骨架线网眼匹配多源面状居民地

多源大比例尺城市地图中,同名居民地数据间往往存在较大几何位置偏差,从而增加了居民地匹配的难度和不确定性。针对这一问题,本文提出了一种利用空白区域骨架线网眼进行居民地匹配的新方法。首先,提取空白区域骨架线,并建立空白区域骨架线网眼和居民地之间的一一映射关系,将居民地匹配转换为骨架线网眼匹配;然后,根据骨架线网眼之间的相接拓扑关系构建对偶图,计算对偶图中每个节点的各项中心性指标,并利用极化变换和层次分析法建立骨架线网眼匹配模型,获取骨架线网眼匹配结果;最后,将骨架线网眼匹配结果按照映射关系进行传递,从而得到居民地匹配结果。将居民地匹配转换为空白区域骨架线网眼匹配,并对骨架线网眼进行对偶图构建和极化变换,为匹配增加拓扑约束和相对位置约束,从而弥补几何位置的较大偏差对匹配造成的影响。对比试验及分析表明本方法能够有效解决大比例尺城市地图中几何位置偏差较大的面状居民地的匹配问题。     

线实体候选匹配集的优化方法研究

地图数据合并是地理空间数据集成的基本途径,同名实体匹配是其中的重点与难点。本文根据线实体的形状将其分为简单线实体与复杂线实体,提出针对前者以线实体端点与中点为发生元生成的Voronoi图所得到的邻近对应关系为依据、针对后者以基于线实体缓冲区重叠度构造的相似性测度指标为依据优化候选匹配集的思路,并设计与实现了相关算法。实证研究表明,该算法能够适应不同比例尺与不同时相的城市道路网地图数据同名实体匹配。     

一种基于形状上下文特征匹配的线状要素Morphing方法

提出了一种基于上下文特征的形状匹配方法,并将其用于线状要素的Morphing变换。首先通过计算每个点的形状上下文,建立形状直方图,然后通过直方图匹配找到同名实体在大小比例尺下轮廓点的最佳匹配关系。根据点的匹配关系,得到对应线段。最后通过分段线性内插实现线状要素的连续尺度变换。实验结果表明,基于形状上下文的轮廓点集匹配方法不需要标志点或者关键点,适应性较强,可以有效地实现形状匹配,极大地提高Morphing变换的精度。     

一种改进形状上下文特征匹配的线要素Morphing方法

提出了一种改进形状上下文特征匹配的线要素Morphing方法。该方法通过对小比例尺线要素进行点加密,使得点对之间的相似性评估不受点集数目差异的影响,仅与线上点的分布形态有关,并对原始形状上下文外围空间区域划分进行加密,使其在描述点的形状上下文时能更充分顾及外围点的影响;然后通过Kuhn-Munkres算法使得最终匹配结果在权重之和最大的同时达到全局最优;最后对匹配结果进一步进行调整,通过线性内插实现Morphing变换。试验表明,该方法与原有利用单一形状上下文进行特征匹配的方法相比,能有效避免Morphing变换过程中线要素自相交情况的出现,且在Morphing变换过程中能较好的保持线要素的形态特征。     

Optimal spatial data matching for conflation: A network flow‐based approach

Spatial data conflation involves the matching and merging of counterpart features in multiple datasets. It has applications in practical spatial analysis in a variety of fields. Conceptually, the feature‐matching problem can be viewed as an optimization problem of seeking a match plan that minimizes the total discrepancy between datasets. In this article, we propose a powerful yet efficient optimization model for feature matching based on the classic network flow problem in operations research. We begin with a review of the existing optimization‐based methods and point out limitations of current models. We then demonstrate how to utilize the structure of the network‐flow model to approach the feature‐matching problem, as well as the important factors for designing optimization‐based conflation models. The proposed model can be solved by general linear programming solvers or network flow solvers. Due to the network flow formulation we adopt, the proposed model can be solved in polynomial time. Computational experiments show that the proposed model significantly outperforms existing optimization‐based conflation models. We conclude with a summary of findings and point out directions of future research.     

A Snake-based Approach for TIGER Road Data Conflation

The TIGER (Topologically Integrated Geographic Encoding and Referencing) system has served the U.S. Census Bureau and other agencies' geographic needs successfully for two decades. Poor positional accuracy has however made it extremely difficult to integrate TIGER with advanced technologies and data sources such as GPS, high resolution imagery, and state/local GIS data. In this paper, a potential solution for conflation of TIGER road centerline data with other geospatial data is presented. The first two steps of the approach (feature matching and map alignment) remain the same as in traditional conflation. Following these steps, a third is added in which active contour models (snakes) are used to automatically move the vertices of TIGER roads to high-accuracy roads, rather than transferring attributes between the two datasets. This approach has benefits over traditional conflation methodology. It overcomes the problem of splitting vector road line segments, and it can be extended for vector imagery conflation as well. Thus, a variety of data sources (GIS, GPS, and Remote Sensing) could be used to improve TIGER data. Preliminary test results indicate that the three-step approach proposed in this paper performs very well. The positional accuracy of TIGER road centerline can be improved from an original 100 plus meters' RMS error to only 3 meters. Such an improvement can make TIGER data more useful for much broader application.     

Linear feature conflation: An optimization-based matching model with connectivity constraints

Geospatial data conflation is the process of combining multiple datasets about a geographic phenomenon to produce a single, richer dataset. It has received increased research attention due to its many applications in map making, transportation, planning, and temporal geospatial analyses, among many others. One approach to conflation, attempted from the outset in the literature, is the use of optimization-based conflation methods. Conflation is treated as a natural optimization problem of minimizing the total number of discrepancies while finding corresponding features from two datasets. Optimization-based conflation has several advantages over traditional methods including conciseness, being able to find an optimal solution, and ease of implementation. However, current optimization-based conflation methods are also limited. A main shortcoming with current optimized conflation models (and other traditional methods as well) is that they are often too weak and cannot utilize the spatial context in each dataset while matching corresponding features. In particular, current optimal conflation models match a feature to targets independently from other features and therefore treat each GIS dataset as a collection of unrelated elements, reminiscent of the spaghetti GIS data model. Important contextual information such as the connectivity between adjacent elements (such as roads) is neglected during the matching. Consequently, such models may produce topologically inconsistent results. In this article, we address this issue by introducing new optimization-based conflation models with structural constraints to preserve the connectivity and contiguity relation among features. The model is implemented using integer linear programming and compared with traditional spaghetti-style models on multiple test datasets. Experimental results show that the new element connectivity (ec-bimatching) model reduces false matches and consistently outperforms traditional models.     

地图合并的技术研究

本文探讨了地图合并技术的研究内容、研究范畴、一般流程;讨论了地图合并的概念、方法;论述了地图合并的主要内容;重点综述了地图合并的相关算法.这些问题的研究丰富完善了地图合并技术的基本理论体系.     

Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches

Map matching is a widely used technology for mapping tracks to road networks. Typically, tracks are recorded using publicly available Global Navigation Satellite Systems, and road networks are derived from the publicly available OpenStreetMap project. The challenge lies in resolving the discrepancies between the spatial location of the tracks and the underlying road network of the map. Map matching is a combination of defined models, algorithms, and metrics for resolving these differences that result from measurement and map errors. The goal is to find routes within the road network that best represent the given tracks. These matches allow further analysis since they are freed from the noise of the original track, they accurately overlap with the road network, and they are corrected for impossible detours and gaps that were present in the original track. Given the ongoing need for map matching in mobility research, in this work, we present a novel map matching method based on Markov decision processes with Reinforcement Learning algorithms. We introduce the new Candidate Adoption feature, which allows our model to dynamically resolve outliers and noise clusters. We also incorporate an improved Trajectory Simplification preprocessing algorithm for further improving our performance. In addition, we introduce a new map matching metric that evaluates direction changes in the routes, which effectively reduces detours and round trips in the results. We provide our map matching implementation as Open Source Software (OSS) and compare our new approach with multiple existing OSS solutions on several public data sets. Our novel method is more robust to noise and outliers than existing methods and it outperforms them in terms of accuracy and computational speed.     

针对简化路网的基于序列的双向合并地图匹配算法

简化路网具有结构简单、算法运行速度快的特点,对地图匹配算法的基础研究具有应用价值。本文针对简化路网的GPS轨迹数据地图匹配问题提出了一种基于序列的双向合并算法。实验结果表明:本算法不仅具有较好的匹配度,还优于现有的Passby算法和增量算法,并且在匹配过程中解决了遗漏路段和极值等错误问题。所以,本算法对于实际路网的地图匹配具有较好的借鉴作用。     

Automatic Road Change Detection and GIS Updating from High Spatial Remotely-Sensed Imagery

This paper presents a tramework for road network change detectlon In order to upctate the Canadian National Topographic DataBase (NTDB). The methodology has been developed on the basis of road extraction from IRS-pan images (with a 5.8 m spatial resolution) by using a wavelet approach. The feature matching and conflation techniques are used to road change detection and updating. Elementary experiments have showed that the proposed framework could be used for developing an operational road database updating system.