Review for Examining the Oxidation Process of the Moon Using Generative Adversarial Networks: Focusing on Landscape of Moon

Abstract

Japan Aerospace Exploration Agency (JAXA) has collected and studied the data observed by the lunar probe, SELenological and ENgineering Explorer (SELENE), from 2007 to 2017. JAXA discovered that the oxygen of the upper atmosphere of the Earth is transported to the moon by the tail of the magnetic field. However, this research is still in progress, and more data are needed to clarify the oxidation process. Therefore, this paper supplements the insufficient observation data by using Generative Adversarial Networks (GAN) and proposes a review paper focusing on the methodology, enhancing the level of completion of the preceding research, and the trend of examining the oxidation process and landscape of the moon. We propose using Anokhin’s Conditionally-Independent Pixel Synthesis (CIPS) as a model to be used in future experiments as a result of the review. CIPS can generate pixels independently for each color value, and since it uses a Multi-Layer Perceptron (MLP) network rather than spatial convolutions, there is a significant advantage in scalability. It is concluded that the proposed methodology will save time and costs of the existing research in progress and will help reveal the causal relationship more clearly.

Keywords

• GAN
• AI
• landscape
• landscape of moon
• moon land data