Organizations: College of Computer Science, Beijing University of Technology · Zhengzhou University · Zhejiang University · Beijing Institute of Technology · Beijing Electronic Science and Technology Institute · Intelligent Science & Technology Academy of CASIC
Cross-view object geo-localization (CVOGL) locates a target in satellite imagery using drone or street-view queries. Existing methods train separate detectors for each viewpoint, leading to parameter redundancy and impeding cross-view knowledge sharing. Moreover, top-ranked satellite candidates are often visually similar, so visual appearance and categorical labels alone are insufficient to resolve such ambiguity. To address these, we propose MVLGeo, an efficient framework designed to unify multiple viewpoints and reduce model redundancy. First, we introduce environmental contextual text from the query view as cues to distinguish visually similar candidates via Vision-Language Reranking (VL-Rerank). Second, we design a multi-view Mixture-of-Experts architecture (MV-MoE) with a shared encoder and view-specific experts to reduce redundancy and promote knowledge sharing, while cross-view contrastive learning aligns their representations for consistency. Third, we introduce an adaptive elliptical prior (ESAM-Prior) as auxiliary positional encoding for anisotropic geometric perception. Extensive experiments on the CVOGL benchmarks confirm that MVLGeo, as a unified model for multiple query viewpoints, achieves state-of-the-art performance, demonstrating robustness to input degradation and generalization across viewpoints. Code and models will be available on GitHub to facilitate future work.
Figures & tables
Figure 1: Illustration of the VL-Rerank framework. A VLM generates environmental context (e.g., road topology) from query images. These descriptions rerank satellite candidates, filtering out visually similar but contextually mismatched bounding boxes and identifying the correct target.
Figure 2: Comparison of the proposed MV-MoE and independent models. MV-MoE shares one encoder and two lightweight experts (UAV, SVI) for four input variants (UAV Vis, UAV Gray, SVI Vis, SVI Gray), reducing parameters by 38% while maintaining Acc@50 (%).
Figure 3: Schematic diagram of the MVLGeo network architecture. (A) Input-guided features are generated using SAM and shape priors. (B) Balancing heterogeneous input channels. (C–D) Using a Mixture-of-Experts (MoE) architecture with hard gating to perform cross-view feature extraction, fusion, and detection from UAV and SVI perspectives. (E) Aligning cross-view features during the training phase via InfoNCE contrastive learning. (F) Performing multi-image comparison and reranking by combining environmental semantic descriptions and image cropping to output the Top-1 matching result.
Figure 4: Recall@K reveals that the ground-truth box often appears among top candidates but is not ranked first.
Figure 5: Shape prior generation pipeline. (a) Query image, (b) SAM mask and fitted ellipse, (c) Anisotropic Gaussian heatmap.
Ground → Satellite
Drone → Satellite
Method
Validation
Test
Validation
Test
Models
Acc@25%
Acc@50%
Acc@25%
Acc@50%
Acc@25%
Acc@50%
Acc@25%
Acc@50%
CVM-Net [ 11 ]
5.09
0.87
4.73
0.51
20.04
3.47
20.14
3.29
2
RK-Net [ 18 ]
8.67
0.98
7.40
0.82
19.94
3.03
19.22
2.67
2
L2LTR [ 35 ]
12.24
1.84
10.69
2.16
38.68
5.96
38.95
6.27
2
Polar-SAFA [ 26 ]
19.18
2.71
20.66
3.19
36.19
6.39
37.41
6.58
2
Table 1: Comparison of Acc@25/50 (%) and number of models on the CVOGL dataset. Bold denotes the best results.
Method
Params
Models
Total
DetGeo
73.8M
2
147.6M
OCGNet
74.8M
2
149.6M
MVLGeo (Ours)
91.0M
1
91.0M
Table 2: Comparison of model parameters and deployment costs between existing dual-detector methods and our single unified model.
VL- Rerank
MV- MoE
ESAM- Prior
UAV Acc@50 (%)
SVI Acc@50 (%)
Params
×
×
×
57.66
42.24
73.8M × 2
×
×
✓
62.58
46.76
73.8M × 2
×
✓
×
52.14
41.35
91.0M × 1
×
✓
✓
56.63
44.09
91.0M × 1
✓
✓
✓
66.04
47.24
91.0M × 1
Table 3: Ablation study of each component in MVLGeo on the UAV and SVI test sets.
Figure 6: Contributions of each component to the UAV and SVI test sets, and corresponding parameter relationships.
#
CL λ
BN γ init
Val Best
Test Best
1
0.1
1.0 (identity)
49.29
50.36
2
0.1
Random
44.74
43.91
3
0.5
1.0
47.52
46.87
4
0.0
1.0
49.59
49.23
Table 4: Average Acc@50 (%) across UAV and SVI on validation and test sets under MV-MoE configurations.
Figure 7: Cross-dataset localization accuracy distribution on UAV and SVI scenes. The yellow value line denotes the Pareto trade-off boundary, and points above the line achieve superior overall performance.
Figure 8: ESAM-Prior ablation results on UAV and SVI datasets. (a) Validation Acc@50 (%) over training epochs. (b) Acc@50 (%) across object aspect ratio buckets partitioned into Low, Medium and High groups.
Test modality
Single baseline
MV-MoE unified
Retention
UAV Vis (UV)
62.6
56.6
90.4%
UAV Gray (UG)
62.4
54.5
87.3%
SVI Vis (SV)
46.8
44.0
94.0%
SVI Gray (SG)
45.9
44.1
96.1%
4-View Avg
—
49.8
—
Table 5: Unified MV-MoE generalizes across four modality-viewpoint combinations with minimal accuracy degradation under grayscale inputs.