  In recent years, street view imagery has grown to become one of the most
important sources of geospatial data collection and urban analytics, which
facilitates generating meaningful insights and assisting in decision-making.
Synthesizing a street-view image from its corresponding satellite image is a
challenging task due to the significant differences in appearance and viewpoint
between the two domains. In this study, we screened 20 recent research papers
to provide a thorough review of the state-of-the-art of how street-view images
are synthesized from their corresponding satellite counterparts. The main
findings are: (i) novel deep learning techniques are required for synthesizing
more realistic and accurate street-view images; (ii) more datasets need to be
collected for public usage; and (iii) more specific evaluation metrics need to
be investigated for evaluating the generated images appropriately. We conclude
that, due to applying outdated deep learning techniques, the recent literature
failed to generate detailed and diverse street-view images.
