A Review of Ground-to-Aerial Cross-View Localization Research

HU Di; YUAN Xia; XU Xiaoqiang; ZHAO Chunxia

doi:10.11999/JEIT250167

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HU Di, YUAN Xia, XU Xiaoqiang, ZHAO Chunxia. A Review of Ground-to-Aerial Cross-View Localization Research[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250167

Citation:

HU Di, YUAN Xia, XU Xiaoqiang, ZHAO Chunxia. A Review of Ground-to-Aerial Cross-View Localization Research[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250167

HU Di, YUAN Xia, XU Xiaoqiang, ZHAO Chunxia. A Review of Ground-to-Aerial Cross-View Localization Research[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250167

Citation:

HU Di, YUAN Xia, XU Xiaoqiang, ZHAO Chunxia. A Review of Ground-to-Aerial Cross-View Localization Research[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250167

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A Review of Ground-to-Aerial Cross-View Localization Research

doi: 10.11999/JEIT250167 cstr: 32379.14.JEIT250167

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Received Date: 2025-03-17
Accepted Date: 2025-12-17
Rev Recd Date: 2025-12-16

Available Online: 2025-12-25

Abstract

Abstract

Significance This paper presents a comprehensive review of ground-to-aerial cross-view localization, systematically organizes representative methods, benchmark datasets, and evaluation metrics. Notably, it is the first review to systematically organize ground-to-aerial cross-view localization algorithms that integrate range sensors, such as Light Detection and Ranging (LiDAR) and millimeter-wave radar, thereby providing new perspectives for subsequent research. Ground-to-aerial cross-view localization has emerged as a key topic in computer vision, aiming to determine the precise pose of ground-based sensors by referencing aerial imagery. This technology is increasingly applied in autonomous driving, unmanned aerial vehicle navigation, intelligent transportation systems, and urban management. Despite substantial progress, ground-to-aerial cross-view localization continues to face major challenges arising from temporal and spatial variations, including seasonal changes, day-night transitions, weather conditions, viewpoint differences, and scene layout changes. These factors require more robust and accurate algorithms to reduce localization errors. This review summarizes the state of the art and provides a forward-looking discussion of challenges and research directions. Progress Ground-to-aerial cross-view localization has advanced rapidly, particularly through the integration of range sensors such as LiDAR and millimeter-wave radar, which has opened new research directions and application scenarios. The development of this field can be divided into several stages. Early studies rely on manually designed features, marking a transition from same-view localization to cross-view geographic localization. With the emergence of deep learning, metric learning, image transformation, and image generation methods are adopted to learn correspondences between images captured from different viewpoints. However, many deep learning models exhibit limited robustness to temporal and spatial variations, especially in long-term cross-season scenarios in which visual appearances at the same location differ markedly across seasons. Additionally, the large-scale nature of urban environments presents difficulties for efficient image retrieval and matching. Range sensors provide accurate distance measurements and three-dimensional structural information, which support reliable localization in scenes where visual cues are weak or absent. Nevertheless, effective fusion of range-sensor data and visual data remains challenging because of discrepancies in spatial resolution, sampling frequency, and sensing coverage. Conclusions This paper reviews the evolution of ground-to-aerial cross-view localization technologies, analyzes major technical advances and their driving factors at different stages. From an algorithmic perspective, the main categories of ground-to-aerial cross-view localization methods are systematically discussed to provide a clear theoretical framework and technical overview. The role of benchmark datasets in promoting progress in this field is highlighted by comparing the performance of representative models across datasets, thereby clarifying differences and relative advantages among methods. Although notable progress has been achieved, several challenges persist, including cross-region localization accuracy, precise localization over large-scale aerial imagery, and sensitivity to temporal changes in geographic features. Further research is required to improve the robustness, accuracy, and efficiency of localization systems. Prospects Future research on ground-to-aerial cross-view localization is expected to concentrate on several directions. Greater attention should be paid to transform range-sensor data into feature representations that align effectively with image features, enabling efficient cross-modal information fusion. Multi-branch network architectures, in which different modalities are processed separately and then fused, may support richer feature extraction. Graph-based models may also be explored to capture shared semantics between ground and aerial views and to support information propagation across modalities. In addition, algorithms that adapt to seasonal variation, day-night cycles, and changing weather conditions are required to enhance robustness and localization accuracy. The integration of multi-scale and multi-temporal data may further improve adaptability to spatio-temporal variation, for example through the combination of images with different spatial resolutions or acquisition times. For large-scale urban environments, efficient search and matching strategies remain essential. Parallel computing frameworks may be applied to manage large datasets and accelerate retrieval, whereas algorithmic strategies such as pruning can reduce computational redundancy and improve matching efficiency. Overall, although ground-to-aerial cross-view localization continues to face challenges, it shows substantial potential for further methodological development and practical deployment.
- Ground-to-aerial cross-view localization,
- Computer vision,
- Deep learning,
- Range sensors,
- Multimodal

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