Abstract: This paper propose a continuous generalization method supported by DTW algorithm for the continuous scale transformation of linear map features. Based on the idea of scale fusion, this method takes two different geometric representations of the same geographical entity at large and small scales as input. Firstly, the corresponding relationship between the coordinate vertices of the two geometric representations is established based on the DTW algorithm, and then the geometric data at any mesoscale is dynamically derived using the linear interpolation method to achieve continuous cartographic generalization. The correctness of the correspondence between vertices directly determines the quality of the linear interpolation results. The geometric representation of the same entity at different scales often has different coordinate points, and the correspondence between vertices is one to many. In order to find the optimal vertex matching, the DTW algorithm is used to solve the optimal matching with the vertex distance as the matching cost and the overall minimum distance as the objective function. Experiment results show that the vertex matching method based on DTW can adapt to different map generalization scenes, and the cartographic generalization effect supported by this method can achieve continuous, smooth and gradual changes, which conforms to the cartographic representation rules and human spatial cognition.

Key words: dynamic time warping, cartographic generalization, scale transformation, continuous generalization