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Proceedings Paper

Generating satisfactory terrain by terrain maker generative adversarial nets
Author(s): Yiqing He; Kai Xie; Tong Li; Xingyu Sun; Ting Li; Shilong Chen
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Paper Abstract

Generative Adversarial Networks (GANs) is one of the most promising generative model in recently years. In this paper, we proposed a model called terrain maker Generative Adversarial Networks (TMGAN). It differs from the original GANs in three points: first, based on given topographic map, TMGAN can generate corresponding satellite aerial map, and vice versa. Second, TMGAN can modeled the terrain adaptively. Third, TMGAN can predict the height map of surface environment. We collected two data sets of paired and unpaired topographic maps and satellite aerial maps to train our model and test the influence of hidden variables. In this paper, we demonstrate the three-dimensional modeling ability of TMGAN.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 114320Q (14 February 2020); doi: 10.1117/12.2536635
Show Author Affiliations
Yiqing He, Beijing Institute of Graphic Communication (China)
Kai Xie, Beijing Institute of Graphic Communication (China)
Tong Li, Beijing Institute of Graphic Communication (China)
Xingyu Sun, Beijing Institute of Graphic Communication (China)
Ting Li, Beijing Institute of Graphic Communication (China)
Shilong Chen, Beijing Institute of Graphic Communication (China)


Published in SPIE Proceedings Vol. 11432:
MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Zhiguo Cao; Jie Ma; Zhong Chen; Yu Shi, Editor(s)

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