SMAPGAN: Generative Adversarial Network Based Semi-Supervised Styled Map Tiles Generating Method

• URL: http://arxiv.org/abs/2001.07712v2
• Date: Thu, 1 Apr 2021 11:47:26 GMT
• Title: SMAPGAN: Generative Adversarial Network Based Semi-Supervised Styled Map Tiles Generating Method
• Authors: X. Chen (1), S. Chen (1), T. Xu (1), B. Yin (1), X. Mei (2), J. Peng (2), H. Li (2) ((1) School of Computer Science, Wuhan University, Wuhan, China, (2) School of Geosciences and Info-Physics, Central South University, Changsha, China)
• Abstract summary: Traditional online map tiles, widely used on the Internet such as Google Map and Baidu Map, are rendered from vector data. We propose a semi-supervised Generation of styled map Tiles based on Generative Adversarial Network (SMAPGAN) model. Experimental results present that SMAPGAN outperforms state-of-the-art (SOTA) works according to mean squared error, structural similarity index, and ESSI.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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