Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model (DEM) and vector hydrographic lines is a common practice in spatial analysis and mapping, no method for automated conflation between these spatial data types has been developed so far. The problem of DEM and hydrography misalignment arises not only in map compilation, but also during the production of generalized datasets. There is a lack of automated solutions which can ensure that the drainage network represented in the surface of generalized DEM is spatially adjusted with independently generalized vector hydrography. We propose a new method that performs the conflation of DEM with linear hydrographic data and is embeddable into DEM generalization process. Given a set of reference hydrographic lines, our method automatically recognizes the most similar paths on DEM surface called counterpart streams. The elevation data extracted from DEM is then rubbersheeted locally using the links between counterpart streams and reference lines, and the conflated DEM is reconstructed from the rubbersheeted elevation data. The algorithm developed for extraction of counterpart streams ensures that the resulting set of lines comprises the network similar to the network of ordered reference lines. We also show how our approach can be seamlessly integrated into a TIN-based structural DEM generalization process with spatial adjustment to pre-generalized hydrographic lines as additional requirement. The combination of the GEBCO_2019 DEM and the Natural Earth 10M vector dataset is used to illustrate the effectiveness of DEM conflation both in map compilation and map generalization workflows. Resulting maps are geographically correct and are aesthetically more pleasing in comparison to a straightforward combination of misaligned DEM and hydrographic lines without conflation.

中文翻译：

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数字高程模型与参考水文线自动合并

合并来自不同来源的未对齐的空间数据会使空间分析和地图创建变得复杂。合并是通过空间调整或两个数据集中相似特征之间的属性转移来解决错位问题的过程。尽管数字高程模型（DEM）和矢量水文测绘线的组合是空间分析和制图的一种常见做法，但迄今为止，尚未开发出用于在这些空间数据类型之间进行自动合并的方法。DEM和水文坐标未对准的问题不仅出现在地图编辑中，而且还出现在生成通用数据集的过程中。缺乏自动化的解决方案，该解决方案无法确保使用独立的广义矢量水文在空间上调整广义DEM表面表示的排水网络。我们提出了一种将DEM与线性水文数据进行合并的新方法，并将其嵌入到DEM泛化过程中。给定一组参考水文线，我们的方法会自动识别DEM表面上最相似的路径，称为对应流。然后，使用对应流和参考线之间的链接在本地对从DEM提取的高程数据进行橡胶处理，并从橡胶高程数据中重建合并的DEM。为提取对应流而开发的算法可确保生成的线集包含与有序参考线网络相似的网络。我们还展示了如何将我们的方法无缝集成到基于TIN的结构DEM泛化过程中，并将空间调整到预先概括的水文测绘线作为附加要求。GEBCO_2019 DEM和Natural Earth 10M向量数据集的组合用于说明DEM合并在地图编辑和地图泛化工作流中的有效性。与没有对齐的DEM和水文线的直接组合相比，生成的地图在地理上是正确的，并且在美学上更令人愉悦。