  Traditional online map tiles, widely used on the Internet such as Google Map
and Baidu Map, are rendered from vector data. Timely updating online map tiles
from vector data, of which the generating is time-consuming, is a difficult
mission. It is a shortcut to generate map tiles in time from remote sensing
images, which can be acquired timely without vector data. However, this mission
used to be challenging or even impossible. Inspired by image-to-image
translation (img2img) techniques based on generative adversarial networks
(GAN), we proposed a semi-supervised Generation of styled map Tiles based on
Generative Adversarial Network (SMAPGAN) model to generate styled map tiles
directly from remote sensing images. In this model, we designed a
semi-supervised learning strategy to pre-train SMAPGAN on rich unpaired samples
and fine-tune it on limited paired samples in reality. We also designed image
gradient L1 loss and image gradient structure loss to generate a styled map
tile with global topological relationships and detailed edge curves of objects,
which are important in cartography. Moreover, we proposed edge structural
similarity index (ESSI) as a metric to evaluate the quality of topological
consistency between generated map tiles and ground truths. Experimental results
present that SMAPGAN outperforms state-of-the-art (SOTA) works according to
mean squared error, structural similarity index, and ESSI. Also, SMAPGAN won
more approval than SOTA in the human perceptual test on the visual realism of
cartography. Our work shows that SMAPGAN is potentially a new paradigm to
produce styled map tiles. Our implementation of the SMAPGAN is available at
https://github.com/imcsq/SMAPGAN.
