SDU-G2A: Self-Derived Uncertainty Guidance for Ground-to-Aerial Remote-Sensing Image Synthesis with Probabilistic BEV Footprints

Urban planning and land-use monitoring require not only semantic land-cover maps, but also image-like overhead observations that preserve the spatial organization of urban scenes. Conventional remote-sensing segmentation and image-generation methods usually operate by obtaining available overhead imagery or in reliable overhead conditions. Street-level panoramas are easier to collect and update, but they observe the target scene from a limited terrestrial viewpoint. Recovering overhead information from a single panorama, therefore, requires inferring from an unobserved land-cover footprint before synthesizing a nadir-view remote-sensing image. We propose SDU-G2A, a two-stage framework for aerial image synthesis from a single ground panorama. Stage I predicts a probabilistic BEV land-cover footprint from the input panorama. The proposed Azimuthal-Radial Mapper propagates azimuthal evidence and applies distance-aware supervision to improve semantic recovery in occluded and far-field regions. Stage II converts the inferred footprint into a remote-sensing-like aerial image through diffusion-based synthesis, making the reconstructed urban structure available in an overhead-image form for visual interpretation and geospatial-correspondence evaluation. The main difficulty is that the inferred BEV footprint exhibits spatially varying reliability. Treating it as a deterministic condition can force cross-view segmentation errors into the generated image. SDU-G2A, therefore, derives a pixel-wise uncertainty map from Stage I predictions and uses it as a spatial reliability layer. This design preserves structures with high prediction confidence while allowing uncertain regions to be completed under text-guided and prior-driven generation. Experiments on VIGORv2 show that SDU-G2A improves BEV land-cover estimation, reduces radial prediction errors, and achieves stronger geospatial correspondence and aerial-feature distributional realism than representative G2A baselines. These results indicate that self-derived land-cover uncertainty can bridge cross-view semantic footprint inference and reliable remote-sensing image synthesis from accessible street-level observations.