  We synthesize both optical RGB and synthetic aperture radar (SAR) remote
sensing images from land cover maps and auxiliary raster data using generative
adversarial networks (GANs). In remote sensing, many types of data, such as
digital elevation models (DEMs) or precipitation maps, are often not reflected
in land cover maps but still influence image content or structure. Including
such data in the synthesis process increases the quality of the generated
images and exerts more control on their characteristics. Spatially adaptive
normalization layers fuse both inputs and are applied to a full-blown generator
architecture consisting of encoder and decoder to take full advantage of the
information content in the auxiliary raster data. Our method successfully
synthesizes medium (10 m) and high (1 m) resolution images when trained with
the corresponding data set. We show the advantage of data fusion of land cover
maps and auxiliary information using mean intersection over unions (mIoUs),
pixel accuracy, and Fr\'echet inception distances (FIDs) using pretrained U-Net
segmentation models. Handpicked images exemplify how fusing information avoids
ambiguities in the synthesized images. By slightly editing the input, our
method can be used to synthesize realistic changes, i.e., raising the water
levels. The source code is available at https://github.com/gbaier/rs_img_synth
and we published the newly created high-resolution dataset at
https://ieee-dataport.org/open-access/geonrw.
