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This paper focuses on learning complex polyline generalization. First, the requirements for sampled images to ensure the effective learning of complex polyline generalization are analysed. To meet these requirements, new methods for segmenting complex polylines and sampling images are proposed. Second, using the proposed segmentation and sampling method, a use case for the learning of complex polyline generalization using the generative adversarial network model, Pix2Pix, is developed. Third, this use case is applied experimentally for the complex generalization of coastline data from a scale of 1:50,000 to 1:250,000. Additionally, contrast experiments are conducted to compare the proposed segmentation and sampling method with object-based and traditional fixed-size methods. Experimental results show that the images generated using the proposed method are superior to the other two methods in the learning and application of complex polyline generalization. The results generalized for the developed use case are globally reasonable and suitably accurate.

Segmentation and sampling method for complex polyline generalization based on a generative adversarial network