ABSTRACT

The growth of georeferenced data sources calls for advanced matching methods to improve the reliability of geospatial data processing, such as map conflation. Existing matching methods mainly focus on similarity measures at the entity scale or area scale. A measure that combines entity-scale and area-scale similarities can provide sound matching results under various circumstances. In this paper, we propose a georeferenced-graph model that integrates multiscale similarities for data matching. Specifically, a match of correspondent data objects is identified by the entity-scale measure under the constraint of the area-scale measure. Nodes in the proposed georeferenced graph model represent polygons by their centroids, whereas the links in the graph connect the nodes (i.e. centroids) according to pre-defined rules. Then, we develop an algorithm to identify many-to-many matches. We demonstrate the proposed graph model and algorithm in real-world experiments using OpenStreetMap data. The experimental results show that the proposed georeferenced-graph model can effectively integrate the context and the location-and-form distance of geospatial data matches across different datasets.

摘要

地理参考数据源的增长需要先进的匹配方法来提高地理空间数据处理的可靠性，例如地图合并。现有的匹配方法主要集中在实体尺度或区域尺度上的相似性度量。结合实体尺度和区域尺度相似性的度量可以在各种情况下提供合理的匹配结果。在本文中，我们提出了一种地理参考图模型，该模型集成了用于数据匹配的多尺度相似性。具体而言，在面积尺度度量的约束下，通过实体尺度度量来识别对应数据对象的匹配。建议的地理参考图模型中的节点通过其质心表示多边形，而图中的链接根据预定义的规则连接节点（即质心）。然后，我们开发了一种算法来识别多对多匹配。我们使用 OpenStreetMap 数据在实际实验中演示了所提出的图模型和算法。实验结果表明，所提出的地理参考图模型可以有效地整合不同数据集的地理空间数据匹配的上下文和位置和形式距离。