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A Survey of Measures and Methods for Matching Geospatial Vector Datasets

机译:匹配地理空间矢量数据集的措施和方法的概述

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The field of Geographical Information Systems (GIS) has experienced a rapid and ongoing growth of available sources for geospatial data. This growth has demanded more data integration in order to explore the benefits of these data further. However, many data providers implies many points of view for the same phenomena: geospatial features. We need sophisticated procedures aiming to find the correspondences between two vector datasets, a process named geospatial data matching. Similarity measures are key-tools for matching methods, so it is interesting to review these concepts together. This article provides a survey of 30 years of research into the measures and methods facing geospatial data matching. Our survey presents related work and develops a common taxonomy that permits us to compare measures and methods. This study points out relevant issues that may help to discover the potential of these approaches in many applications, like data integration, conflation, quality evaluation, and data management.
机译:地理信息系统(GIS)领域的地理空间数据可用资源正在快速且持续增长。这种增长要求更多的数据集成,以便进一步探索这些数据的好处。但是,许多数据提供者针对同一现象暗示了许多观点:地理空间特征。我们需要复杂的过程来寻找两个矢量数据集之间的对应关系,这个过程称为地理空间数据匹配。相似性度量是匹配方法的关键工具,因此一起回顾这些概念很有趣。本文对地理空间数据匹配面临的措施和方法进行了30年的研究调查。我们的调查介绍了相关工作,并制定了一个通用分类法,使我们可以比较措施和方法。这项研究指出了相关问题,这些问题可能有助于发现这些方法在许多应用中的潜力,例如数据集成,合并,质量评估和数据管理。

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