Organizations: School of Electronic Information, Wuhan University, Wuhan 430072, China · School of Artificial Intelligence, Wuhan University, Wuhan 430072, China · Technology and Engineering Center for Space Utilization and the Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing 100094, China · School of Aeronautics and Astronautics, University of Chinese Academy of Sciences, Beijing 100049, China
Cross-view geo-localization for Unmanned Aerial Vehicles (UAVs) operating in GNSS-denied environments remains challenging due to the severe geometric discrepancy between oblique UAV imagery and orthogonal satellite maps. Most existing methods address this problem through a decoupled pipeline of place retrieval and pose estimation, implicitly treating perspective distortion as appearance noise rather than an explicit geometric transformation. In this work, we propose a geometry-aware UAV geo-localization framework that explicitly models the 3D scene geometry to unify coarse place recognition and fine-grained pose estimation within a single inference pipeline. Our approach reconstructs a local 3D scene from multi-view UAV image sequences using a Visual Geometry Grounded Transformer (VGGT), and renders a virtual Bird's-Eye View (BEV) representation that orthorectifies the UAV perspective to align with satellite imagery. This BEV serves as a geometric intermediary that enables robust cross-view retrieval and provides spatial priors for accurate 3 Degrees of Freedom (3-DoF) pose regression. To efficiently handle multiple location hypotheses, we introduce a Satellite-wise Attention Block that isolates the interaction between each satellite candidate and the reconstructed UAV scene, preventing inter-candidate interference while maintaining linear computational complexity. In addition, we release a recalibrated version of the University-1652 dataset with precise coordinate annotations and spatial overlap analysis, enabling rigorous evaluation of end-to-end localization accuracy. Extensive experiments on the refined University-1652 benchmark and SUES-200 demonstrate that our method significantly outperforms state-of-the-art baselines, achieving robust meter-level localization accuracy and improved generalization in complex urban environments.
Figures & tables
Figure 1: Framework Comparison. We show the comparison with existing two-stage UAV geo-localization methods and our unified UAV geo-localization framework.
Figure 2: Overview of the unified UAV geo-localization pipeline. Given a sequence of oblique UAV query images, the pipeline extracts the representation using VGGT to reconstruct a local 3D scene. Based on this shared representation, a satellite-aligned BEV is rendered to support coarse-level satellite retrieval. The retrieved satellite candidates are then independently aligned with the reconstructed 3D scene within the same feature space, enabling fine-grained regression of the absolute 3-DoF UAV pose (GPS coordinates and heading).
Figure 3: Satellite-wise Attention Block. The proposed satellite-wise attention block lets the satellite tokens attend to the UAV tokens independently, followed by the candidate validations.
Figure 4: Similarity Score of Features.
Figure 5: Geometric Relationship between UAV Camera and Satellite Tiles. Visualization of the University-Pose dataset geometry. The red arrow represents the 3-DoF UAV pose. Notably, the dataset contains spatially overlapped satellite tiles, providing dense candidates that facilitate robust pose estimation.
Figure 6: Overlapped Satellite Images Statistic.
Methods
University-1652
SUES-200 (150m)
R@1 ↑
R@5 ↑
R@10 ↑
AP ↑
R IoU @1 ↑
R IoU @5 ↑
Time(s) ↓
R@1 ↑
AP ↑
University ( Zheng et al., 2020 )
69.3
86.7
91.2
73.1
-
-
0.8
67.8
72.7
AnyLoc ( Keetha et al., 2023 )
36.2
59.1
57.3
36.2
37.2
60.2
1.9
30.0
42.7
SegVLAD ( Garg et al., 2024 )
56.8
68.1
76.9
56.9
60.2
69.3
16.1
56.0
59.1
UCVGL ( Li et al., 2025 )
54.5
63.2
77.9
66.2
57.1
74.1
62.3
56.0
60.5
Ours (Initial BEV)
58.8
78.7
84.2
63.2
63.2
79.2
3.5
56.5
61.6
Table 1: Retrieval Result on University-1652 and SUES-200.
IoU Threshold τ
R IoU@1↑
R IoU@5↑
R IoU@10↑
0.1
84.9
89.2
89.9
0.2
83.9
88.4
89.4
0.5
80.6
88.3
89.4
0.8
79.9
88.2
89.3
1.0
79.0
88.2
89.3
Table 2: IoU-based Metric on Retrieval Results.
Figure 7: Visualization of the BEV Refinement.
Refine Time
R@1 ↑
R@5 ↑
R@10 ↑
AP ↑
0 (Initial BEV)
58.8
78.7
84.2
63.2
1
66.8
84.0
87.9
70.6
2
79.0
88.0
89.3
81.1
3
80.5
89.2
89.2
82.2
Table 3: Retrieval Results of BEV View Refinement.
Figure 8: Curve of Meter-level Position Success Rate. Meter-level Success Rate (MSR@X) is the percentage of queries localized within the X-meter threshold.
View Num.
E^RMSE↓
E^median↓
Δθ^RMSE↓
Δθ^median↓
4
4.36
2.71
1.84
1.24
12
4.65
2.85
1.96
1.03
18
6.93
2.91
3.37
1.07
Table 4: Result of Relative Pose Evaluation. We report the 3-DoF Relative Pose Error (RPE) of UAV frames.
(a)
(b)
(c)
R@1 ↑
Emedian↓
Δθmean↓
Δθmedian↓
67.2
60.1
22.9
5.2
✓
67.7
57.1
17.3
4.3
✓
71.6
42.1
19.3
4.2
✓
✓
72.3
37.8
20.7
4.8
✓
✓
79.0
26.2
12.2
4.1
Table 5: Ablation Study of the Proposed Modules. (a) Satellite-wise Attention (b) Geometric Validation (c) Feature Validation .
View Number
R@1 ↑
R@5 ↑
R@10 ↑
AP ↑
4
64.9
79.0
82.9
68.2
12
79.0
88.0
89.3
81.1
18
77.8
86.4
90.6
83.1
Table 6: Impact of the Number of Input Images.
Figure 9: Overlapped Satellite Images Distribution Comparison.
College of Computer Science and Technology, Zhejiang University · State Key Laboratory of Blockchain and Data Security, Zhejiang University · Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China · Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, Changsha 410073, China · School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China