Traditional online map tiles, which are widely used on the Internet, such as by Google Maps and Baidu Maps, are rendered from vector data. The timely updating of online map tiles from vector data, for which generation is time-consuming, is a difficult mission. Generating map tiles over time from remote sensing images is relatively simple and can be performed quickly without vector data. However, this approach used to be challenging or even impossible. Inspired by image-to-image translation (img2img) techniques based on generative adversarial networks (GANs), we proposed a semisupervised generation of styled map tiles based on the GANs (SMAPGAN) model to generate styled map tiles directly from remote sensing images. In this model, we designed a semisupervised learning strategy to pretrain SMAPGAN on rich unpaired samples and fine-tune it on limited paired samples in reality. We also designed the image gradient L1 loss and the image gradient structure loss to generate a styled map tile with global topological relationships and detailed edge curves for objects, which are important in cartography. Moreover, we proposed the edge structural similarity index (ESSI) as a metric to evaluate the quality of the topological consistency between the generated map tiles and ground truth. The experimental results show that SMAPGAN outperforms state-of-the-art (SOTA) works according to the mean squared error, the structural similarity index, and the ESSI. Also, SMAPGAN gained higher approval than SOTA in a human perceptual test on the visual realism of cartography. Our work shows that SMAPGAN is a new tool with excellent potential for producing styled map tiles. Our implementation of SMAPGAN is available at https://github.com/imcsq/SMAPGAN .

中文翻译：

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SMAPGAN：基于生成对抗网络的半监督样式化地图拼贴生成方法

传统的在线地图图块是从矢量数据中绘制的，这些图块在Internet上得到了广泛使用，例如Google Maps和Baidu Maps。从矢量数据中及时更新在线地图图块是一项艰巨的任务，因为矢量数据的生成非常耗时。随着时间的推移从遥感图像生成地图图块相对简单，并且无需向量数据即可快速执行。但是，这种方法曾经具有挑战性，甚至是不可能的。受基于生成对抗网络（GAN）的图像到图像转换（img2img）技术的启发，我们提出了一种基于GAN（SMAPGAN）模型的半监督式样式化地图图块生成，以直接从遥感图像生成样式化的地图图块。在这个模型中 我们设计了一种半监督学习策略，以在丰富的未配对样本上对SMAPGAN进行预训练，并在实际的有限配对样本上对其进行微调。我们还设计了图像梯度L1损失和图像梯度结构损失，以生成样式化的地图图块，该图块具有全局拓扑关系和对象的详细边缘曲线，这在制图中很重要。此外，我们提出了边缘结构相似性指数（ESSI）作为评估生成的地图图块与地面真实性之间的拓扑一致性的质量的度量。实验结果表明，根据均方误差，结构相似性指数和ESSI，SMAPGAN的性能优于最先进的（SOTA）。此外，在关于制图视觉真实性的人类感知测试中，SMAPGAN比SOTA获得了更高的认可。我们的工作表明，SMAPGAN是一种新工具，具有潜力，可以制作样式化的地图图块。我们的SMAPGAN实施可在以下位置获得：https://github.com/imcsq/SMAPGAN 。