PolarGAN: Creating realistic Arctic sea ice concentration images with user-defined geometric preferences

Mingyu Kim, Jaekyeong Lee, Leechan Choi, Minjoo Choi

Abstract: In this paper, we introduce a novel generative adversarial network (GAN), called PolarGAN, that is capable of creating realistic artificial images of Arctic sea ice concentration (SIC) for data augmentation. One of the key features of the PolarGAN is that it considers real-valued geometric preferences, defined by six statistics, to generate SIC images that align with specific geometric characteristics. Unlike other GANs that also consider user-defined preferences, the PolarGAN allows for more detailed control over the shape and size of the generated images by using differentiable projection functions to convert the created images into geometric features, and a newly-designed loss function to minimize the gap between the user-defined preferences and the geometric features of the generated images. Through extensive experimentation, we compare the PolarGAN with other GANs and demonstrate artificial SIC scenarios that can be used to test the performance of algorithms for Arctic route planning in edge cases or to improve data-driven models such as SIC prediction models which require additional data to avoid overfitting issues.