Generating Multiscale Maps From Satellite Images via Series Generative Adversarial Networks

Considering the success of generative adversarial networks (GANs) for image-to-image translation, researchers have attempted to translate satellite images to maps (si2map) through GAN for cartography. However, these studies involved limited scales, which hinders multiscale map creation. By extending their method, high-resolution satellite images can be trivially translated to multiscale maps through scale-wise si2map generators trained for certain scales. However, this strategy has two theoretical limitations. First, inconsistency between high-resolution satellite images and object generalization on multiscale maps (SI-M inconsistency) increasingly complicates the extraction of geographical information from satellite images for generators with decreasing scale. Second, as si2map translation is cross-domain, generators incur high computation costs to transform the pixel distribution on satellite images to that on maps. Thus, we designed a series strategy of generators for multiscale si2map translation to address these limitations. In this strategy, high-resolution satellite images are inputted to an si2map generator to output large-scale maps, which are translated to multiscale maps through series multiscale map generators. The series strategy avoids SI-M inconsistency as high-resolution satellite images are only translated to large-scale maps and transforms cross-domain translation to approximately intradomain translation when generating multiscale maps. Our experimental results showed better quality multiscale map generation with the series strategy, as shown by average increases of 11.69%, 53.78%, 55.42%, and 72.34% in the structural similarity index (SSIM), edge structural similarity index (ESSI), intersection over union (road), and intersection over union (water) for data from Mexico City and Tokyo at zoom levels 17–13.