Analyzing the Impact of Geospatial Derivatives on Domain Adaptation with CycleGAN

CycleGAN, a deep learning model for image conversion tasks is an extension of the Pix2Pix architecture [1] offering cycle consistency loss that offers image-to-image translation where you have two sets of images from different domains. Through deep learning in this way, it can generate high-quality and diverse image transformation results, which are widely used in the fields of style transfer, and domain adaptation. With generative AI taking center stage, the underlying principles of CycleGAN are used extensively across several domains including geospatial analysis. In this study we explore the potential of additional geospatial derivatives that when used can have the potential to enhance CycleGAN’s domain adaptation. The analysis focuses on integrating digital surface model and NDVI to enhance image translation accuracy, maintain content integrity, and optimize CycleGAN for practical applications. Our results show that combining these two with multispectral bands yields better outcomes than isolating them from the training process, highlighting the effectiveness of this synergistic approach.