  Aerial imagery analysis is critical for many research fields. However,
obtaining frequent high-quality aerial images is not always accessible due to
its high effort and cost requirements. One solution is to use the
Ground-to-Aerial (G2A) technique to synthesize aerial images from easily
collectible ground images. However, G2A is rarely studied, because of its
challenges, including but not limited to, the drastic view changes, occlusion,
and range of visibility. In this paper, we present a novel Geometric Preserving
Ground-to-Aerial (G2A) image synthesis (GPG2A) model that can generate
realistic aerial images from ground images. GPG2A consists of two stages. The
first stage predicts the Bird's Eye View (BEV) segmentation (referred to as the
BEV layout map) from the ground image. The second stage synthesizes the aerial
image from the predicted BEV layout map and text descriptions of the ground
image. To train our model, we present a new multi-modal cross-view dataset,
namely VIGORv2 which is built upon VIGOR with newly collected aerial images,
maps, and text descriptions. Our extensive experiments illustrate that GPG2A
synthesizes better geometry-preserved aerial images than existing models. We
also present two applications, data augmentation for cross-view
geo-localization and sketch-based region search, to further verify the
effectiveness of our GPG2A. The code and data will be publicly available.
