Generating satisfactory terrain by terrain maker generative adversarial nets

Yiqing He; Kai Xie; Tong Li; Xingyu Sun; Ting Li; Shilong Chen

doi:10.1117/12.2536635

14 February 2020 Generating satisfactory terrain by terrain maker generative adversarial nets

Yiqing He, Kai Xie, Tong Li, Xingyu Sun, Ting Li, Shilong Chen

Proceedings Volume 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 114320Q (2020) https://doi.org/10.1117/12.2536635
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

Citation Download Citation

Yiqing He, Kai Xie, Tong Li, Xingyu Sun, Ting Li, and Shilong Chen "Generating satisfactory terrain by terrain maker generative adversarial nets", Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 114320Q (14 February 2020); https://doi.org/10.1117/12.2536635

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