Abstract
Satellite images are information rich snapshots of ecosystems and landscapes. In consequence, the features in the images strongly depend on the environmental conditions. Such dependency between climate and landscapes has been regarded since the beginning of earth sciences; however, it has never been taken as literally as in the present study. We adapted a deep learning generative model as a first demonstration of the potential behind deep learning for spatial pattern generation in geoscience. The purpose is to build a conditional Generative Adversarial Network (cGAN) useful to establish the relationship between two loosely linked set of variables that show multitude of complex spatial features such as climate conditions to aerial image. We trained a custom cGAN to generate Sentinel-2 multispectral imagery given a set of climatic and terrain predictors. Results show that the generated imagery shares many characteristics with the real one. In some cases, the quality of the generated imagery is high enough to deceive humans. We envision that such use of deep learning for geoscience could become an important tool to test the effects of climate on landscapes and ecosystems. © 2018 IEEE.
