Cross-view geo-localization remains challenging due to drastic appearance variations and geometric distortions caused by viewpoint changes. Existing methods primarily rely on discriminative feature learning or explicit geometric alignment, yet often overlook an important observation: humans tend to match scenes by integrating the spatial context of multiple landmarks rather than relying solely on isolated local visual cues. Inspired by this, we propose a Spatial Context Modeling (SCM) module, which traverses feature maps along four orthogonal directions and models interactions among the resulting directional representations at each spatial position. By aggregating complementary directional observations, SCM constructs richer scene layout representations. Since reliable spatial context depends on robust visual features, we introduce a High-Frequency Information Guidance (HFIG) module that extracts stable structural cues from the frequency domain. By emphasizing edges and contours that are less sensitive to appearance variations, HFIG encourages the model to learn more robust representations. Extensive experiments demonstrate that our method achieves competitive performance on standard cross-view geo-localization benchmarks while exhibiting strong robustness under more challenging scenarios with unknown orientations and limited fields of view (FoVs).
Figures & tables
Figure 1: Illustration of the motivation behind our method. Existing methods mainly rely on discriminative appearance features, which will lead to misalignment due to view shifts. When viewpoints change, the relative spatial arrangement of scene elements provides more stable cues than isolated appearance features. Our method aggregates complementary observations from four scanning directions to build richer scene layout representations for robust cross-view alignment.
Figure 2: Overview of the proposed method. Illustration of the High-frequency Information Guidance (HFIG) and Spatial Context Modeling (SCM). The HFIG module converts the image into the frequency domain using DCT and then extracts fine-grained information of high-frequency. SCM captures the holistic spatial layout via position-wise multi-head attention over four directional scanning paths.
Method
CVUSA
R@1
R@5
R@10
R@1%
SAFA †
89.84
96.93
98.14
99.64
TransGeo
94.08
98.36
99.04
99.77
GeoDTR †
95.43
98.86
99.34
99.86
PaSS-KD †
94.09
98.42
99.13
99.77
Sample4Geo
98.68
99.68
99.78
99.87
Table 1: Performance comparison on CVUSA dataset. Best results are in bold, second-best in underlined. “-” means the score is not provided in the original paper.
Method
CVACT_val
CVACT_test
R@1
R@5
R@10
R@1%
R@1
R@5
R@10
R@1%
SAFA †
81.03
92.80
94.84
98.17
55.50
79.94
85.08
94.49
TransGeo
84.95
94.14
95.78
98.37
-
-
-
-
GeoDTR †
86.21
95.44
96.72
98.77
64.52
88.59
91.96
98.74
PaSS-KD †
87.20
94.30
95.67
97.85
66.81
88.03
90.87
98.02
Sample4Geo
90.81
96.74
97.48
98.77
71.51
92.42
94.45
98.70
Table 2: Performance comparison on CVACT dataset. † indicates applying polar transform to aerial images. Best results in bold, second-best in underlined. “-” means the score is not provided in the original paper.
Set
Methods
FoV= 360∘
FoV= 180∘
FoV= 90∘
FoV= 70∘
R@1
R@5
R@10
R@1%
R@1
R@5
R@10
R@1%
R@1
R@5
R@10
R@1%
R@1
R@5
R@10
R@1%
CVUSA
CVM
16.25
38.86
49.41
88.11
7.38
22.51
32.63
75.38
2.76
10.11
16.74
55.49
2.62
9.30
15.06
21.77
CVFT
23.38
44.42
55.20
86.64
8.10
24.25
34.47
75.15
4.80
14.84
23.18
61.23
3.79
12.44
19.33
55.56
DSM
78.11
89.46
92.90
98.50
48.53
68.47
75.63
93.02
16.19
31.44
39.85
71.13
8.78
19.90
27.30
61.20
SEH
85.37
93.51
95.81
-
53.73
72.32
78.96
-
16.58
32.17
40.34
-
7.82
18.84
25.59
-
L2LTR
-
-
-
-
56.69
80.86
87.75
98.01
26.92
50.49
60.41
86.88
13.95
33.07
43.86
77.65
Table 3: Comparison of images with unknown orientation and different FoVs on the CVACT and CVUSA datasets. ‘’-” means the score is not provided in the original paper. The best performance is in bold and the second-best performance is underline. Note that the model is trained and tested on FoV-specific images.
Methods
CVACT_val
CVACT_test
R@1
R@5
R@10
R@1%
R@1
R@5
R@10
R@1%
Baseline
85.37
94.83
96.23
98.50
66.50
87.94
90.08
98.49
Baseline + SCM
90.21
96.07
96.99
98.47
71.90
91.71
93.73
98.36
Baseline + HFIG
89.80
95.69
96.67
98.30
71.72
91.68
93.46
98.32
Baseline + SCM + HFIG (Ours)
91.02
96.83
97.53
98.86
72.28
92.69
94.47
98.69
Table 4: Ablation study on CVACT dataset. We evaluate the Effectiveness of each component in our method. Best results are in bold.
strategie
CVACT_val
CVACT_test
R@1
R@5
R@10
R@1%
R@1
R@5
R@10
R@1%
Repeated Scan
90.14
96.38
97.29
98.66
71.01
91.68
93.68
98.40
Bidirectional Scan
90.29
95.94
96.92
98.55
71.34
91.86
93.95
98.45
Cross Scan
90.38
96.06
97.01
98.45
71.84
91.69
93.66
98.36
Rotation Scan (Ours)
91.02
96.83
97.53
98.86
72.28
92.69
94.47
98.69
Table 5: Effectiveness of scanning strategies in SCM. Best results are in bold.
Figure 3: Heatmap visualization of the Baseline and SLGeo model. Best viewed on screen with zoom-in.