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.

本文着重于学习复杂的折线综合。首先，分析了对采样图像的要求，以确保有效学习复杂的折线泛化。为了满足这些要求，提出了分割复杂折线和采样图像的新方法。其次，使用提出的分割和采样方法，开发了使用生成对抗网络模型Pix2Pix学习复杂折线综合的用例。第三，该用例被实验性地应用于海岸线数据从1：50,000到1：250,000的复杂概括。此外，进行对比实验，以将建议的分割和采样方法与基于对象的和传统的固定大小方法进行比较。实验结果表明，该方法生成的图像在复杂折线综合的学习和应用中优于其他两种方法。对于已开发用例而言，概括的结果在全球范围内都是合理的，并且是准确的。