Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance

Ahmad Arrabi, Xiaohan Zhang, Waqas Sultani, Chen Chen, Safwan Wshah; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5356-5366

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 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 dataset are available at https://github.com/AhmadArrabi/GPG2A