SCC-Loc: A Unified Semantic Cascade Consensus Framework for UAV Thermal Geo-Localization

Xiaoran Zhang; Yu Liu; Jinyu Liang; Kangqiushi Li; Zhiwei Huang; Huaxin Xiao

cs.CVApr 3, 2026

SCC-Loc: A Unified Semantic Cascade Consensus Framework for UAV Thermal Geo-Localization

Authors: Xiaoran Zhang, Yu Liu, Jinyu Liang, Kangqiushi Li, Zhiwei Huang, Huaxin Xiao

Organizations: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Abstract

Cross-modal Thermal Geo-localization (TG) provides a robust, all-weather solution for Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments. However, profound thermal-visible modality gaps introduce severe feature ambiguity, systematically corrupting conventional coarse-to-fine registration. To dismantle this bottleneck, we propose SCC-Loc, a unified Semantic-Cascade-Consensus localization framework. By sharing a single DINOv2 backbone across global retrieval and MINIMA $_{\text{RoMa}}$ matching, it minimizes memory footprint and achieves zero-shot, highly accurate absolute position estimation. Specifically, we tackle modality ambiguity by introducing three cohesive components. First, we design the Semantic-Guided Viewport Alignment (SGVA) module to adaptively optimize satellite crop regions, effectively correcting initial spatial deviations. Second, we develop the Cascaded Spatial-Adaptive Texture-Structure Filtering (C-SATSF) mechanism to explicitly enforce geometric consistency, thereby eradicating dense cross-modal outliers. Finally, we propose the Consensus-Driven Reliability-Aware Position Selection (CD-RAPS) strategy to derive the optimal solution through a synergy of physically constrained pose optimization. To address data scarcity, we construct Thermal-UAV, a comprehensive dataset providing 11,890 diverse thermal queries referenced against a large-scale satellite ortho-photo and corresponding spatially aligned Digital Surface Model (DSM). Extensive experiments demonstrate that SCC-Loc establishes a new state-of-the-art, suppressing the mean localization error to 9.37 m and providing a 7.6-fold accuracy improvement within a strict 5-m threshold over the strongest baseline. Code and dataset are available at https://github.com/FloralHercules/SCC-Loc.

Figures & tables

Fig. 1: Conceptual comparison of the existing approach and our SCC-Loc framework. To overcome severe cross-modal bottlenecks in thermal geo-localization, SCC-Loc systematically resolves: (a) spatial quantization bias via semantic alignment (SGVA); (b) dense structural outliers via cascaded filtering (C-SATSF); and (c) deceptive visual decoys via consensus selection (CD-RAPS).

Fig. 2: Overview of the proposed SCC-Loc framework: (1) shared DINOv2 feature extraction for UAV thermal and satellite images; (2) SGVA-based viewport alignment; (3) C-SATSF-based correspondence purification after MINIMA ${}_{\text{RoMa}}$ matching; and (4) CD-RAPS-based position selection via pose optimization, reliability evaluation, and geographic consensus.

Fig. 3: Visual samples from the constructed Thermal-UAV dataset. The dataset systematically captures profound modality discrepancies and diurnal thermal variations across diverse spatial topologies: (a) Urban and (b) Rural Scenes showcase thermal UAV queries of distinct semantic categories (e.g., buildings, roads, fields) during daytime and nighttime; (c) Reference Map presents the global database at varying search scales, comprising visible-light satellite ortho-photos and their spatially aligned Digital Surface Models (DSM) to supply crucial 3D elevation priors.

Fig. 4: Qualitative visualization of the proposed SCC-Loc pipeline in (a) Urban and (b) Rural scenarios. The process illustrates the adaptive correction of spatial quantization bias via the SGVA module ( Re-Cropping ) and the progressive suppression of structural outliers using the C-SATSF mechanism ( Matching ). Finally, based on the purified correspondences, the CD-RAPS strategy refines candidate poses via physically constrained optimization, and computes the total reliability ( Final score ) by fusing the multi-dimensional evaluation ( Base score ) with geographic consensus to determine the robust optimal hypothesis for precise Localization .

Coarse Retrieval Phase
Hyperparameter Description	Symbol	Value
Geographic search area	$A_{\text{search}}$	$600\times 600\text{ m}^{2}$
Sliding window overlap	$\rho_{\text{overlap}}$	$60\%$
GSD scaling factor	$s_{\text{gsd}}$	$1.5$
Minimum error tolerance	$\epsilon_{\text{min}}$	$10^{-6}$
Pooling exponent	$\psi$	$4$

TABLE I: Detailed hyperparameter configurations for the proposed SCC-Loc framework.

Method

Top-3 Candidates

Top-5 Candidates

Top-10 Candidates

Recall@3

Acc@5/10/20

\pm

Time

Recall@5

Acc@5/10/20

\pm

Time

Recall@10

Acc@5/10/20

\pm

Time

CAMP + XoFTR

75.15

1.53 / 5.96 / 16.51

280.52\pm 374.93

1.03

85.70

1.66 / 6.94 / 20.04

193.75\pm 306.82

1.24

96.13

1.66 / 8.13 / 23.79

121.00\pm 216.86

1.79

CAMP + RoMa

75.15

3.06 / 12.04 / 31.49

91.89\pm 139.31

4.01

85.70

4.26 / 14.00 / 36.98

69.00\pm 105.93

6.30

96.13

4.38 / 15.83 / 39.70

49.17\pm 82.95

11.97

CAMP + MINIMA

{}_{\text{RoMa}}

75.15

4.72 / 15.57 / 40.43

84.26\pm 131.58

4.22

85.70

4.77 / 17.62 / 46.17

61.79\pm 111.69

6.60

96.13

6.77 / 21.11 / 49.83

40.92\pm 78.34

12.53

DINOv2 + XoFTR

91.23

1.66 / 7.57 / 21.45

185.70\pm 307.10

0.92

96.77

2.17 / 8.64 / 24.51

141.61\pm 254.98

1.30

99.45

2.30 / 9.57 / 25.87

100.86\pm 188.14

1.96

DINOv2 + RoMa

91.23

3.57 / 14.98 / 39.28

55.21\pm 81.07

4.28

96.77

4.09 / 16.81 / 43.45

48.04\pm 70.72

6.36

99.45

4.89 / 16.85 / 42.21

40.50\pm 56.01

12.02

TABLE II: Quantitative comparison of the proposed SCC-Loc framework against various two-stage baselines, which comprise different combinations of retrieval and matching models. The best results are highlighted in bold .

Method	Search Area: $600\times 600$			Search Area: $800\times 800$			Search Area: $1200\times 1200$			Memory
Method	Acc@5/10/20	ME $\pm$ SD	Time	Acc@5/10/20	ME $\pm$ SD	Time	Acc@5/10/20	ME $\pm$ SD	Time	Memory
NIVnet	0.10 / 0.80 / 2.60	$91.64\pm 35.06$	0.02	0.10 / 0.60 / 2.10	$92.37\pm 35.63$	0.03	0.20 / 0.70 / 1.90	$99.21\pm 47.49$	0.04	15
STHN	0.50 / 3.10 / 11.50	$40.70\pm 15.49$	0.03	0.70 / 3.10 / 12.10	$38.76\pm 16.57$	0.03	1.00 / 3.40 / 12.00	$49.60\pm 44.40$	0.04	5.5
DINOv2 + MINIMA ${}_{\text{RoMa}}$	6.85 / 22.85 / 54.09	$31.32\pm 51.18$	12.28	6.26 / 20.89 / 52.51	$36.92\pm 73.59$	12.50	5.11 / 19.06 / 48.13	$69.05\pm 148.08$	13.62	2335.5
SCC-Loc (Ours)	52.09 / 86.38 / 93.53	$\mathbf{9.37\pm 25.26}$	14.21	51.40 / 81.70 / 89.87	$\mathbf{16.84\pm 55.63}$	14.30	46.30 / 76.00 / 83.45	$\mathbf{46.62\pm 127.04}$	16.12	1174.7

TABLE III: Comparison with domain-specific baselines across varying search areas on our Thermal-UAV dataset under the Top-10 retrieval setting. Best results are in bold .

Fig. 5: Sensitivity analysis of the roll and pitch penalty weights in physically constrained non-linear pose optimization under the Top-5 retrieval setting. (a) Acc@5, (b) Acc@10, and (c) mean error (ME) under different combinations of $\lambda_{\mathrm{roll}}$ and $\lambda_{\mathrm{pitch}}$ .

Components			Performance Metrics
SGVA	C-SATSF	CD-RAPS	Recall@5	Acc@5/10/20	ME $\pm$ SD	Time
✓			96.98	5.91 / 23.49 / 56.38	33.74 $\pm$ 61.53	6.81
	✓		96.72	7.15 / 21.87 / 53.40	37.40 $\pm$ 68.48	6.82
		✓	96.72	17.40 / 46.47 / 74.72	26.08 $\pm$ 55.59	8.60
✓	✓		96.98	8.00 / 24.47 / 57.70	32.73 $\pm$ 60.38	7.00
✓		✓	96.98	20.89 / 51.91 / 78.00	21.71 $\pm$ 46.61	8.58

TABLE IV: Ablation study of the proposed framework under the Top-5 retrieval setting. The best results are highlighted in bold .

Stage	Description	Time (s)
Retrieval	Top-10 candidate search	0.769
SGVA	Semantic viewport alignment	0.001
Dense Matching	MINIMA ${}_{\text{RoMa}}$ matching	11.917
C-SATSF	Cascaded correspondence filtering	0.087
CD-RAPS	Pose optimization and selection	1.355
Other	Data conversion and I/O overhead	0.081

TABLE V: Runtime breakdown of SCC-Loc under the Top-10 retrieval setting.

SGVA Parameters		Performance Metrics
$\alpha$	$\beta$	Recall@5	Acc@5/10/20	ME $\pm$ SD
0	1.0	96.89	50.47 / 79.32 / 87.62	16.99 $\pm$ 48.01
	1.2	96.94	49.66 / 77.79 / 87.83	17.80 $\pm$ 50.78
	1.5	96.98	51.49 / 79.23 / 87.36	16.54 $\pm$ 46.29
0.2	1.0	96.98	51.66 / 81.32 / 89.87	15.30 $\pm$ 44.54
	1.2	97.11	52.00 / 82.60 / 89.91	14.75 $\pm$ 43.66

TABLE VI: Sensitivity analysis of SGVA parameters under the Top-5 retrieval setting. The best results are highlighted in bold .

$(w_{1},w_{2},w_{3},w_{4})$	Acc@5	Acc@10	Acc@20	ME $\pm$ SD
(0.25, 0.25, 0.25, 0.25)	50.17	80.60	89.23	15.27 $\pm$ 42.93
(0.1, 0.3, 0.3, 0.3)	50.68	81.40	89.74	14.90 $\pm$ 43.07
(0.1, 0.2, 0.35, 0.35)	51.66	81.32	89.87	15.30 $\pm$ 44.54
(0.2, 0.2, 0.3, 0.3)	51.15	81.62	89.45	14.97 $\pm$ 42.52
(0.1, 0.1, 0.4, 0.4)	53.40	82.81	90.00	15.12 $\pm$ 44.55

TABLE VII: Sensitivity analysis of CD-RAPS reliability weights under the Top-5 setting. The best results are highlighted in bold .

Noise Config		Performance Metrics
$\delta_{\text{pitch}}$	$\delta_{\text{yaw}}$	Recall@5	Acc@5/10/20	ME $\pm$ SD
0	0	96.98	51.66 / 81.32 / 89.87	15.30 $\pm$ 44.54
	10	97.36	51.02 / 82.00 / 89.70	15.42 $\pm$ 44.24
	20	97.36	52.34 / 81.91 / 89.06	15.48 $\pm$ 43.87
10	0	96.72	28.98 / 51.74 / 71.45	21.83 $\pm$ 44.71
	10	97.19	28.17 / 51.66 / 70.51	22.31 $\pm$ 44.87

TABLE VIII: Robustness evaluation of the proposed framework under varying pitch and yaw angles in the Top-5 retrieval setting.

Cross-View Geo-Localization

Cross-view geo-localization matches ground, drone, or aerial images to maps or satellite views to estimate where they were captured.

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Oct 10, 2026

ScalLoc: Continuous Oblique Thermal UAV Geo-Localization with a 2D Satellite Map

Cross-modal Thermal Geo-localization (TG) provides an illumination-robust positioning solution for Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments. However, existing thermal geo-localization methods mainly focus on near-nadir observations or depend on map-side elevation priors, limiting their applicability to continuous oblique thermal localization with only geo-referenced 2D satellite maps. To address this problem, we propose ScalLoc, a scale-aligned local-to-map localization framework for continuous oblique thermal UAV geo-localization. ScalLoc follows a reconstruction--refinement--anchoring--propagation design: it reconstructs local window geometry from thermal sequences, refines camera poses using track-based pose optimization, adaptively anchors each window to the satellite map, and propagates validated states to recover frame-level global trajectories. To support systematic evaluation, we construct Thermal-Oblique, a real thermal UAV sequence dataset containing 216 urban and rural day--night trajectories and 28,112 thermal frames with frame-level annotations. Extensive experiments demonstrate that ScalLoc reduces the mean localization error from 112.69 m to 26.97 m compared with the VGGT-only configuration, corresponding to a 76.07% reduction, and achieves the lowest frame-weighted mean position error among the compared methods. These results demonstrate the feasibility of continuous oblique thermal geo-localization using only lightweight 2D satellite maps, without requiring digital surface models or preconstructed 3D maps. Code and dataset are available at https://github.com/FloralHercules/ScalLoc.

Xiaoran Zhang, Yu Liu, Jinyu Liang +1

College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Cross-View Geo-Localization GNSS-Denied Localization UAV Navigation Map-Based Localization Thermal Imaging

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Oct 9, 2026

Structure-Aware Matching for Robust UAV Thermal-to-Satellite Geo-Localization

UAV thermal-to-satellite geo-localization localizes UAVs by matching real-time thermal images against georeferenced satellite images, enabling autonomous navigation in GNSS-denied and low-visibility conditions. However, substantial cross-modal disparities in color, brightness and viewpoint hinder conventional appearance-based methods from establishing reliable correspondences. While existing approaches predominantly address global modality shifts, they overlook the invariant scene structures inherently shared between thermal and satellite views. To bridge this gap, we propose SAMF, a Structure-Aware Matching Framework that explicitly exploits stable cross-modal structural cues to improve UAV thermal geolocalization. Specifically, the Directional Structure Enhancement Module (DSEM) strengthens robust structural representations by capturing directional and low-frequency spatial patterns, and the Modality-Aware Affine Remapping Module (MARM) subsequently applies affine correction to these enhanced features, further promoting cross-modal alignment. Additionally, we introduce the Thermal-UAV-Paired benchmark derived from the Thermal-UAV dataset. Extensive experiments on both the Boson-nighttime and Thermal-UAV-Paired datasets show that SAMF achieves state-of-the-art performance. Ablation studies further validate the effectiveness and synergistic complementarity of DSEM and MARM. The code and dataset are released at: https://github.com/YuxinWang879/SAMF.git.

Chun Liu, Yuxin Wang, Zhenxiang Huo +3

State Key Laboratory of Spatial Datum, Henan University, Zhengzhou 450046, China · College of Remote Sensing and Geoinformatics Engineering, Henan University, Zhengzhou 450046, China · School of Computer and Information Engineering, Henan University, Zhengzhou 450046, China +1

Cross-View Geo-Localization Cross-Modal Feature Matching UAV Navigation

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Apr 29, 2026

Lights Out: A Nighttime UAV Localization Framework Using Thermal Imagery and Semantic 3D Maps

Reliable backup localization for unmanned aerial vehicles (UAVs) operating in GNSS-denied nighttime conditions remains an open challenge due to the severe modality gap between daytime RGB maps and nighttime thermal imagery. This work presents a semantic reprojection framework for map-relative nighttime UAV localization by aligning segmented thermal observations with a globally referenced, semantically labeled 3D map constructed from daytime RGB data. Rather than relying on appearance-based correspondence, localization is formulated in a shared semantic domain and solved via a symmetric bidirectional reprojection objective with confusion-aware weighting to improve robustness under segmentation uncertainty. The approach is evaluated offline across 6.5 km of nighttime, real-world UAV flight trajectories in urban and semi-structured environments. Relative to RTK GNSS ground truth, the system achieves a bias-corrected RMSE2D of 2.18 m and a median RMSE2D of 1.52 m. Results show that localization performance is strongly correlated with the availability of semantic edge evidence and that large-error events are spatially localized to semantically ambiguous areas rather than uniformly distributed. These findings indicate that semantic reprojection offers a promising pathway toward globally referenced nighttime UAV localization using thermal imagery alone.

Ryan Allen, Melissa Greeff

Robora Lab (www.roboralab.com), Queen’s University · affiliated with Ingenuity Labs Research Institute

UAV Navigation GNSS-Denied Localization Map-Based Localization

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