Measuring the similarity between counterpart geospatial features is crucial in the effective conflation of spatial datasets from difference sources. This article proposes a new similarity metric called the “map turning function distance” (MTFD) for matching linear features such as roads based on the well-known turning function (TF) distance in computer vision. The MTFD overcomes the limitations of the traditional TF distance, such as the inability to handle partial matches and insensitivity to differences in scale and rotation. In particular, the MTFD allows one to: (a) partially match a linear feature to a portion of a larger feature from a certain position of match; and (b) consider both the shape and orientation differences of polylines based on comparing their turning angles. In finding the best match position, we prove that the optimal position can be found among a finite set of positions on the target feature. We then combine the MTFD with widely used point-offset distances such as the Hausdorff distance to form a composite similarity metric. Our experiments with real road datasets demonstrate that the new metric has greater discriminative power than traditional point-offset-based similarity measures, and significantly improves the precision of two tested conflation models.

中文翻译：

---

使用转向函数距离合并线性特征：一种新的方向敏感的相似性度量

测量对应地理空间特征之间的相似性对于有效合并来自不同来源的空间数据集至关重要。本文提出了一种新的相似性度量，称为“地图转向函数距离”（MTFD），用于基于计算机视觉中众所周知的转向函数（TF）距离匹配道路等线性特征。MTFD 克服了传统 TF 距离的局限性，例如无法处理部分匹配以及对比例和旋转的差异不敏感。特别是，MTFD 允许： (a) 将线性特征部分匹配到某个匹配位置的较大特征的一部分；(b) 基于比较折线的转角，同时考虑折线的形状和方向差异。在寻找最佳匹配位置时，我们证明可以在目标特征的有限位置集中找到最佳位置。然后，我们将 MTFD 与广泛使用的点偏移距离（例如 Hausdorff 距离）相结合，以形成复合相似度度量。我们对真实道路数据集的实验表明，新度量比传统的基于点偏移的相似性度量具有更大的判别能力，并显着提高了两个测试合并模型的精度。