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SMAPGAN: Generative Adversarial Network-Based Semisupervised Styled Map Tile Generation Method
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DOI：10.1109/TGRS.2020.3021819.png)
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摘要
En 中文
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.
Keyword:
Generative adversarial networks
Image edge detection
Generative adversarial networks (GANs)
map tiles generation
quality assessment
semisupervised
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IF:
8.6
论文数:
2.1W
被引数:
10.7W
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