Cross-view geo-localization (CVGL) aims to accurately localize street-view images through retrieval of corresponding geo-tagged satellite images. While prior works have achieved nearly perfect performance on certain standard datasets, their robustness in real-world corrupted environments remains under-explored. This oversight causes severe performance degradation or failure when images are affected by corruption such as blur or weather, significantly limiting practical deployment. To address this critical gap, we introduce MRGeo, the first systematic method designed for robust CVGL under corruption. MRGeo employs a hierarchical defense strategy that enhances the intrinsic quality of features and then enforces a robust geometric prior. Its core is the Spatial-Channel Enhancement Block, which contains: (1) a Spatial Adaptive Representation Module that models global and local features in parallel and uses a dynamic gating mechanism to arbitrate their fusion based on feature reliability; and (2) a Channel Calibration Module that performs compensatory adjustments by modeling multi-granularity channel dependencies to counteract information loss. To prevent spatial misalignment under severe corruption, a Region-level Geometric Alignment Module imposes a geometric structure on the final descriptors, ensuring coarse-grained consistency. Comprehensive experiments on both robustness benchmark and standard datasets demonstrate that MRGeo not only achieves an average R@1 improvement of 2.92% across three comprehensive robustness benchmarks (CVUSA-C-ALL, CVACT_val-C-ALL, and CVACT_test-C-ALL) but also establishes superior performance in cross-area evaluation, thereby demonstrating its robustness and generalization capability.
Figures & tables
Figure 1: The fragility of existing CVGL models under real-world corruptions. While a model can easily match a clean street-view image to its correct satellite counterpart (top), its performance collapses when the query is affected by common corruptions like weather or blur, often leading to a complete failure in localization (bottom).
Figure 2: Overview of our MRGeo architecture. The framework processes street-view and satellite images through a shared-weight backbone containing our proposed Spatial-Channel Enhancement Block (SCEB) . SCEB enhances feature quality via its two sub-modules: the SARM and the CCM . Finally, the Region-level Geometric Alignment Module (RGAM) imposes a structural constraint on the enhanced features to generate robust final descriptors for retrieval.
Method
Publication
CVUSA-C-ALL
CVACT_val-C-ALL
CVACT_test-C-ALL
R@1
R@5
R@10
R@1%
R@1
R@5
R@10
R@1%
R@1
R@5
R@10
R@1%
L2LTR
NIPS’21
87.93
95.45
97.01
99.01
82.13
93.34
94.93
98.10
57.20
82.59
87.23
98.09
TransGeo
CVPR’22
82.72
91.95
94.03
97.92
74.04
86.19
89.10
94.98
52.18
74.35
78.99
95.03
GeoDTR
AAAI’23
84.64
93.29
95.01
98.24
77.40
88.95
91.28
95.91
52.87
78.84
83.17
95.84
Sample4G
ICCV’23
93.36
97.32
98.08
99.22
86.71
94.35
95.60
98.04
66.33
88.05
90.75
98.07
EP-BEV
ECCV’24
84.33
94.38
96.06
98.39
82.07
92.19
94.09
97.52
59.25
83.12
87.02
97.49
Table 1: Experimental results of cross-view geo-localization methods on comprehensive corruption robustness benchmarks.
Method
Clean
CVACT_val-C
Weather
Blur
Digital
Snow
Frost
Fog
Bright
Spatter
Defocus
Glass
Motion
Zoom
Contrast
Pixel
JPEG
R@ 1cor
L2LTR
84.89
71.03
77.93
83.50
81.17
73.78
83.98
85.07
84.00
49.79
79.15
85.07
83.40
78.16
TransGeo
84.95
47.65
58.51
32.91
72.67
67.13
81.43
84.83
81.80
36.34
22.18
84.92
83.74
62.84
GeoDTR
86.21
48.24
71.74
83.26
84.60
61.39
79.11
85.51
73.44
8.26
55.48
86.01
85.19
68.52
Sample4G
90.81
78.69
84.68
86.97
88.80
84.97
89.17
90.22
89.03
50.00
51.99
90.26
89.52
81.20
Table 2: Experimental results of 7 cross-view geo-localization methods on the CVACT_val-C datasets. We report the R@1 performance of each method under different corruptions (obtained by averaging the 5 corruption severities), as well as the average R@ 1cor under all corruption types.
#
SARM
CCM
RGAM
CVACT_test-C-ALL
R@1
R@5
R@10
R@1%
1
×
×
×
65.84
88.32
91.03
98.30
2
×
×
✓
67.86
89.90
92.26
98.45
3
✓
✓
×
68.90
90.76
92.99
98.56
4
✓
✓
✓
71.16
92.24
94.21
98.80
5
2:1
✓
✓
70.40
91.37
93.38
98.50
Table 3: The Ablation study of Components.
Method
CVUSA → CVACT
CVACT → CVUSA
R@1
R@5
R@1%
R@1
R@5
R@1%
SAFA †
30.40
52.93
85.82
21.45
36.55
69.83
DSM †
33.66
52.17
79.67
18.47
34.46
69.01
TransGeo
37.81
61.57
89.14
18.99
38.24
88.94
L2LTR †
52.58
75.81
93.51
37.69
57.78
89.63
GeoDTR †
53.16
75.62
93.80
44.07
64.66
90.09
Table 4: Cross-area evaluation when trained on the CVUSA dataset and evaluated on CVACT and vice versa. † denotes models that use the polar transformation. – indicates that the corresponding data are not provided. The bottom section shows the impact of the hyperparameter k in SARM on cross-area evaluation.
Figure 3: Few-shot training on CVUSA. Performance evaluation on the raw test set with progressively sampled training subsets (20%, 40%, 60%, 80%, 100%). The red dotted line is MRGeo’s R@1 benchmark on 20% data.
Figure 4: Heatmap visualization of MRGeo’s feature focus on both clean and corrupted images. Best viewed on screen with zoom-in.