An ensemble learning simplification approach based on multiple machine-learning algorithms with the fusion using of raster and vector data and a use case of coastline simplification

To make use of accumulated simplification data and their contained simplification knowledge sufficiently, we propose an intelligent method based on the integration of several machine learning algorithms, which can use vector features and raster images to learn the vertex selection of polyline simplification in this paper. First, vertex selection models based on vector features and raster images are constructed by the fully connected neural network and the convolutional neural network respectively. Trained by corresponding samples, these two models can be utilized to generate vertex selection decisions via inputting vector features or raster images respectively. Second, some fusion models are constructed based on the linear weighting method, naive Bayes method, support vector machine, and artificial neural network to utilize outputs of vector-based and raster-based models to generate better decisions for vertex simplification. Finally, the proposed method applies into a use case. Experimental results show that the vector-based model and the raster-based model can learn and master vertex simplification in different levels, and fusion models can make complementary advantages of raster-based and vector-based models to improve the simplification accuracy further, and the best fusion model is better than some other simplification methods. © 2022, Surveying and Mapping Press. All right reserved.