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.