ARGUS-GS: Sensor-Guided Aerial-Ground Gaussian Splatting for 3D City Modeling with Model-to-World Alignment

3D city modeling requires both broad scene coverage and accurate alignment with the physical world (i.e., model-to-world alignment), for which image-based reconstruction techniques, such as Gaussian Splatting (GS), provide an effective solution. Vehicle and UAV imagery provide complementary coverage, but their large viewpoint gap complicates cross-stream fusion, while accumulated estimated pose errors along the trajectory can further distort the reconstruction away from its true physical location during coordinate alignment. We present ARGUS-GS, a sensor-guided ground-aerial collaborative 3D city modeling system that produces a unified Gaussian representation with model-to-world alignment. Standard onboard sensor measurements anchor each stream's estimated poses and sparse geometry into the shared physical-world metric frame, while revisit handling limits duplicate Gaussian insertion. We then initialize a shared Gaussian field using inverse-depth search with range-adaptive searching windows, maintaining consistent relative search coverage across viewing distances. Camera poses and Gaussian parameters are then jointly optimized using photometric losses and soft sensor-derived poses, with periodic reseeding of poorly reconstructed views. Simulated urban experiments show $5.8$ dB higher PSNR over baselines at ground-truth camera poses, with $66.8$ % lower depth MAE and $87.5$ % higher surface agreement.