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

HU Di; YUAN Xia; XU Xiaoqiang; ZHAO Chunxia

doi:10.11999/JEIT250167

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 >

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

PDF( 3591 KB)

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

Accepted Date: 2025-12-17

Available Online: 2025-12-25

Abstract

Abstract

Significance This paper provides a comprehensive review of ground to aerial cross view localization, systematically organizing the main methods, key datasets, and evaluation metrics. Notably, it is the first to systematically organize ground to aerial cross view localization algorithms combined with range sensors such as LiDAR and millimeter-wave radar, offering new perspectives and insights for future research. Ground to aerial cross view localization has emerged as a pivotal research area in computer vision, bridging the gap between ground-level and aerial imagery to determine the precise pose of ground-based sensors using aerial images as references. This technology is increasingly vital in applications such as autonomous driving, drone navigation, intelligent transportation, and urban management. Despite significant advancements, cross view localization faces substantial challenges due to temporal and spatial variations, such as seasonal changes, day-night transitions, weather conditions, viewpoint alterations, and scene layout modifications. These factors necessitate the development of more robust and accurate algorithms to mitigate localization errors. This paper not only summarizes the state-of-the-art in cross view localization but also provides a forward-looking perspective on the challenges and opportunities in the field. Progress The field of ground to aerial cross view localization has witnessed significant advancements, particularly with the integration of range sensors such as LiDAR and millimeter-wave radar, which has opened new avenues for research and application. The evolution of this field can be categorized into several stages. Initially, traditional methods relied on manually designed features for cross view localization, marking a paradigm shift from same-view to cross view geographic localization strategies. The advent of deep learning introduced a new era, with researchers leveraging metric learning, image transformation, and image generation techniques to learn correspondences between different viewpoint images. Despite these advancements, existing deep learning models often struggled with temporal and spatial variations, particularly in long-term cross-season scenarios where image appearances at the same location varied significantly across seasons. Additionally, the large scale nature of urban environments posed challenges in efficiently searching and matching images. Range sensors, such as LiDAR, provide precise distance measurements and 3D structural information, which are crucial for accurate localization, especially in environments where visual cues are unreliable or unavailable. However, the fusion of data from range sensors with visual data poses significant challenges, including differences in data resolution, frequency, and coverage. Conclusions This paper provides a comprehensive review of the development of ground to aerial cross view localization technologies, analyzing key technological breakthroughs and their underlying driving forces at various stages. It systematically categorizes and discusses the main methods of cross view localization from an algorithmic perspective, aiming to offer readers a clear theoretical framework and technical overview. Notably, the paper emphasizes the role of key datasets in propelling the field forward, comparing the performance of various cross view localization models on these datasets to highlight differences and advantages among methods, thereby providing valuable references for subsequent research. Despite significant advancements, several challenges remain in ground to aerial cross view localization. These include issues related to cross-region localization accuracy, localization precision over large scale aerial images, and the impact of temporal changes on geographic features. These challenges necessitate further research and innovation to enhance the robustness, accuracy, and efficiency of localization systems. Prospects Looking ahead, the future of ground to aerial cross view localization research is poised to focus on several key areas. Future research should delve deeper into effectively converting range sensor data into feature representations that align with image data, facilitating efficient cross-modal information fusion. This could involve the use of multi-branch network architectures where each branch processes a different data modality, followed by a fusion layer that combines these features to extract richer information. Additionally, graph models could be employed to train on shared semantics between ground and aerial views, thereby representing relationships across different data modalities and enabling effective cross-modal information propagation and fusion. There is also a pressing need to develop algorithms that can adapt to variations such as seasonal changes, day-night cycles, and different weather conditions, thereby improving the robustness and accuracy of localization systems. Moreover, integrating multi-scale and multi-temporal data could enhance the model’s adaptability to spatio-temporal variations, for instance, by combining high-resolution and low-resolution images or images captured at different times. For large scale urban environments, research should focus on developing efficient search and matching techniques to enhance localization efficiency. Parallel computing frameworks could be employed to handle large datasets, thereby improving the efficiency of search and matching processes. Algorithmically, strategies such as pruning could be implemented at various stages to reduce unnecessary computations, lower algorithm complexity, and improve matching efficiency on large datasets. In conclusion, while cross view localization faces several challenges, it also holds immense potential for development and application. Future research should continue to explore and innovate in areas such as multi-modal data fusion and efficient search and matching in large scale urban environments to further advance this field.
- Ground-to-aerial cross-view localization,
- Computer vision,
- Deep learning,
- Range sensors,
- Multimodal

FullText(HTML)

References(74)

References

[1]	SHI Yujiao, YU Xin, LIU Liu, et al. Optimal feature transport for cross-view image geo-localization[C]. Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, USA, 2020: 11990–11997. doi: 10.1609/aaai.v34i07.6875.
[2]	WANG Tingyu, ZHENG Zhedong, YAN Chenggang, et al. Each part matters: Local patterns facilitate cross-view geo-localization[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(2): 867–879. doi: 10.1109/TCSVT.2021.3061265.
[3]	WANG Shan, ZHANG Yanhao, PERINCHERRY A, et al. View consistent purification for accurate cross-view localization[C]. Proceedings of 2023 IEEE/CVF International Conference on Computer Vision, Paris, France, 2023: 8163–8172. doi: 10.1109/ICCV51070.2023.00753.
[4]	RODRIGUES R and TANI M. Are these from the same place? Seeing the unseen in cross-view image geo-localization[C]. Proceedings of 2021 IEEE Winter Conference on Applications of Computer Vision, Waikoloa, USA, 2021: 3752–3760. doi: 10.1109/WACV48630.2021.00380.
[5]	HU Di, YUAN Xia, and ZHAO Chunxia. Active layered topology mapping driven by road intersection[J]. Knowledge-Based Systems, 2025, 315: 113305. doi: 10.1016/j.knosys.2025.113305.
[6]	DURGAM A, PAHEDING S, DHIMAN V, et al. Cross-view geo-localization: A survey[J]. IEEE Access, 2024, 12: 192028–192050. doi: 10.1109/ACCESS.2024.3507280.
[7]	ZHANG Kai, YUAN Xia, CHEN Shuntong, et al. Multi-modality semantic-shared cross-view ground-to-aerial localization[C]. Proceedings of the 6th ACM International Conference on Multimedia in Asia, Auckland, New Zealand, 2024: 72. doi: 10.1145/3696409.3700233.
[8]	WILSON D, ZHANG Xiaohan, SULTANI W, et al. Image and object geo-localization[J]. International Journal of Computer Vision, 2024, 132(4): 1350–1392. doi: 10.1007/s11263-023-01942-3.
[9]	张硝, 高艺, 夏宇翔, 等. 跨视角图像地理定位数据集综述[J]. 遥感学报, 2025, 29(8): 2511–2530. doi: 10.11834/jrs.20254348. ZHANG Xiao, GAO Yi, XIA Yuxiang, et al. Review of cross-view image geo-localization datasets[J]. National Remote Sensing Bulletin, 2025, 29(8): 2511–2530. doi: 10.11834/jrs.20254348.
[10]	ASPERTI A, FIORILLA S, NARDI S, et al. A review of recent techniques for person re-identification[J]. Machine Vision and Applications, 2025, 36(1): 25. doi: 10.1007/s00138-024-01622-3.
[11]	ZHANG Yuxin, GUI Jie, CONG Xiaofeng, et al. A comprehensive survey and taxonomy on point cloud registration based on deep learning[C]. Proceedings of the 33rd International Joint Conference on Artificial Intelligence, Jeju, South Korea, 2024: 8344–8353. doi: 10.24963/ijcai.2024/922.
[12]	BANSAL M, SAWHNEY H S, CHENG Hui, et al. Geo-localization of street views with aerial image databases[C]. Proceedings of the 19th ACM International Conference on Multimedia, Scottsdale, USA, 2011: 1125–1128. doi: 10.1145/2072298.2071954.
[13]	BANSAL M, DANIILIDIS K, and SAWHNEY H. Ultrawide baseline facade matching for geo-localization[M]. ZAMIR A R, HAKEEM, A VAN GOOL L, et al. Large-Scale Visual Geo-Localization. Cham: Springer, 2016: 77–98. doi: 10.1007/978-3-319-25781-5_5.
[14]	VO N N and HAYS J. Localizing and orienting street views using overhead imagery[C]. Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 494–509. doi: 10.1007/978-3-319-46448-0_30.
[15]	ZHAI Menghua, BESSINGER Z, WORKMAN S, et al. Predicting ground-level scene layout from aerial imagery[C]. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 4132–4140. doi: 10.1109/CVPR.2017.440.
[16]	MILLER I D, COWLEY A, KONKIMALLA R, et al. Any way you look at it: Semantic crossview localization and mapping with LiDAR[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 2397–2404. doi: 10.1109/LRA.2021.3061332.
[17]	KIM J and KIM J. Fusing lidar data and aerial imagery with perspective correction for precise localization in urban canyons[C]. Proceedings of 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, Macau, China, 2019: 5298–5303. doi: 10.1109/IROS40897.2019.8967711.
[18]	HU Di, YUAN Xia, XI Huiying, et al. Road structure inspired UGV-satellite cross-view geo-localization[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 16767–16786. doi: 10.1109/JSTARS.2024.3457756.
[19]	SHI Qian, HE Da, LIU Zhengyu, et al. Globe230k: A benchmark dense-pixel annotation dataset for global land cover mapping[J]. Journal of Remote Sensing, 2023, 3: 0078. doi: 10.34133/remotesensing.0078.
[20]	GEIGER A, LENZ P, STILLER C, et al. Vision meets robotics: The kitti dataset[J]. The International Journal of Robotics Research, 2013, 32(11): 1231–1237. doi: 10.1177/0278364913491297.
[21]	ZHU Sijie, YANG Taojiannan, and CHEN Chen. VIGOR: Cross-view image geo-localization beyond one-to-one retrieval[C]. Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 5316–5325. doi: 10.1109/CVPR46437.2021.00364.
[22]	WORKMAN S, SOUVENIR R, and JACOBS N. Wide-area image geolocalization with aerial reference imagery[C]. Proceedings of 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3961–3969. doi: 10.1109/ICCV.2015.451.
[23]	黄高爽, 周杨, 胡校飞, 等. 图像地理定位研究进展[J]. 地球信息科学学报, 2023, 25(7): 1336–1362. doi: 10.12082/dqxxkx.2023.230073. HUANG Gaoshuang, ZHOU Yang, HU Xiaofei, et al. A survey of the research progress in image geo-localization[J]. Journal of Geo-information Science, 2023, 25(7): 1336–1362. doi: 10.12082/dqxxkx.2023.230073.
[24]	MA Yuexin, WANG Tai, BAI Xuyang, et al. Vision-centric BEV perception: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12): 10978–10997. doi: 10.1109/TPAMI.2024.3449912.
[25]	周博文, 李阳, 马鑫骥, 等. 深度学习的跨视角地理定位方法综述[J]. 中国图象图形学报, 2024, 29(12): 3543–3563. doi: 10.11834/jig.230858. ZHOU Bowen, LI Yang, MA Xinji, et al. A survey of cross-view geo-localization methods based on deep learning[J]. Journal of Image and Graphics, 2024, 29(12): 3543–3563. doi: 10.11834/jig.230858.
[26]	TOKER A, ZHOU Qunjie, MAXIMOV M, et al. Coming down to earth: Satellite-to-street view synthesis for geo-localization[C]. Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 6484–6493. doi: 10.1109/CVPR46437.2021.00642.
[27]	TANG Hao, LIU Hong, and SEBE N. Unified generative adversarial networks for controllable image-to-image translation[J]. IEEE Transactions on Image Processing, 2020, 29: 8916–8929. doi: 10.1109/TIP.2020.3021789.
[28]	LI Ang, MORARIU V I, and DAVIS L S. Planar structure matching under projective uncertainty for geolocation[C]. Proceedings of the 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 265–280. doi: 10.1007/978-3-319-10584-0_18.
[29]	LIN T Y, BELONGIE S, and HAYS J. Cross-view image geolocalization[C]. Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 891–898. doi: 10.1109/CVPR.2013.120.
[30]	CAO Song and SNAVELY N. Graph-based discriminative learning for location recognition[C]. Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 700–707. doi: 10.1109/CVPR.2013.96.
[31]	WORKMAN S and JACOBS N. On the location dependence of convolutional neural network features[C]. Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, USA, 2015: 70–78. doi: 10.1109/CVPRW.2015.7301385.
[32]	HU Sixing, FENG Mengdan, NGUYEN R M H, et al. CVM-Net: Cross-view matching network for image-based ground-to-aerial geo-localization[C]. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7258–7267. doi: 10.1109/CVPR.2018.00758.
[33]	ZHU Sijie, SHAH M, and CHEN Chen. TransGeo: Transformer is all you need for cross-view image geo-localization[C]. Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 1152–1161. doi: 10.1109/CVPR52688.2022.00123.
[34]	MEI Shaohui, LIAN Jiawei, WANG Xiaofei, et al. A comprehensive study on the robustness of deep learning-based image classification and object detection in remote sensing: Surveying and benchmarking[J]. Journal of Remote Sensing, 2024, 4: 0219. doi: 10.34133/remotesensing.0219.
[35]	SHI Yujiao, LIU Liu, YU Xin, et al. Spatial-aware feature aggregation for cross-view image based geo-localization[C]. Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, Canada, 2019: 905. doi: 10.5555/3454287.3455192.
[36]	MI Li, XU Chang, CASTILLO-NAVARRO J, et al. ConGeo: Robust cross-view geo-localization across ground view variations[C]. Proceedings of the 18th European Conference on Computer Vision, Milan, Italy, 2024: 214–230. doi: 10.1007/978-3-031-72630-9_13.
[37]	ZHANG Xiaohan, LI Xingyu, SULTANI W, et al. Cross-view geo-localization via learning disentangled geometric layout correspondence[C]. Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, USA, 2023: 3480–3488. doi: 10.1609/aaai.v37i3.25457.
[38]	REGMI K and BORJI A. Cross-view image synthesis using geometry-guided conditional GANs[J]. Computer Vision and Image Understanding, 2019, 187: 102788. doi: 10.1016/j.cviu.2019.07.008.
[39]	ZHAO Luying, ZHOU Yang, HU Xiaofei, et al. Street-to-satellite view synthesis for cross-view geo-localization[C]. Proceedings of SPIE 13166, International Conference on Remote Sensing Technology and Survey Mapping, Changchun, China, 2024: 1316608. doi: 10.1117/12.3029089.
[40]	SHI Yujiao, CAMPBELL D, YU Xin, et al. Geometry-guided street-view panorama synthesis from satellite imagery[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(12): 10009–10022. doi: 10.1109/TPAMI.2022.3140750.
[41]	XIA Zimin, BOOIJ O, MANFREDI M, et al. Visual cross-view metric localization with dense uncertainty estimates[C]. Proceedings of the 17th European Conference on Computer Vision, Tel Aviv, Israel, 2022: 90–106. doi: 10.1007/978-3-031-19842-7_6.
[42]	SONG Zhenbo, ZE Xianghui, LU Jianfeng, et al. Learning dense flow field for highly-accurate cross-view camera localization[C]. Proceedings of the 37th International Conference on Neural Information Processing Systems, New Orleans, USA, 2023: 3094. doi: 10.5555/3666122.3669216.
[43]	FERVERS F, BULLINGER S, BODENSTEINER C, et al. Uncertainty-aware vision-based metric cross-view geolocalization[C]. Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023: 21621–21631. doi: 10.1109/CVPR52729.2023.02071.
[44]	TANG T Y, DE MARTINI D, BARNES D, et al. RSL-Net: Localising in satellite images from a radar on the ground[J]. IEEE Robotics and Automation Letters, 2020, 5(2): 1087–1094. doi: 10.1109/LRA.2020.2965907.
[45]	FU Mengyin, ZHU Minzhao, YANG Yi, et al. LiDAR-based vehicle localization on the satellite image via a neural network[J]. Robotics and Autonomous Systems, 2020, 129: 103519. doi: 10.1016/j.robot.2020.103519.
[46]	CHEN Lei, FENG Changzhou, MA Yunpeng, et al. A review of rigid point cloud registration based on deep learning[J]. Frontiers in Neurorobotics, 2024, 17: 1281332. doi: 10.3389/fnbot.2023.1281332.
[47]	TANG T Y, DE MARTINI D, and NEWMAN P. Get to the point: Learning lidar place recognition and metric localisation using overhead imagery[C]. Proceedings of Robotics: Science and Systems 2021, 2021. doi: 10.15607/RSS.2021.XVII.003.(查阅网上资料,未找到出版地信息,请补充).
[48]	TANG T Y, DE MARTINI D, and NEWMAN P. Point-based metric and topological localisation between lidar and overhead imagery[J]. Autonomous Robots, 2023, 47(5): 595–615. doi: 10.1007/s10514-023-10085-w.
[49]	GE Chongjian, CHEN Junsong, XIE Enze, et al. MetaBEV: Solving sensor failures for 3D detection and map segmentation[C]. Proceedings of 2023 IEEE/CVF International Conference on Computer Vision, Paris, France, 2023: 8687–8697. doi: 10.1109/ICCV51070.2023.00801.
[50]	LIU Liu and LI Hongdong. Lending orientation to neural networks for cross-view geo-localization[C]. Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 5617–5626. doi: 10.1109/CVPR.2019.00577.
[51]	MADDERN W, PASCOE G, LINEGAR C, et al. 1 year, 1000 km: The oxford robotcar dataset[J]. The International Journal of Robotics Research, 2017, 36(1): 3–15. doi: 10.1177/0278364916679498.
[52]	MADDERN W, PASCOE G, GADD M, et al. Real-time kinematic ground truth for the oxford robotcar dataset[J]. arXiv: 2002.10152, 2020. doi: 10.48550/arXiv.2002.10152.(查阅网上资料,未能确认文献类型,请确认).
[53]	BARNES D, GADD M, MURCUTT P, et al. The oxford radar robotcar dataset: A radar extension to the oxford RobotCar dataset[C]. Proceedings of 2020 IEEE International Conference on Robotics and Automation, Paris, France, 2020: 6433–6438. doi: 10.1109/ICRA40945.2020.9196884.
[54]	AGARWAL S, VORA A, PANDEY G, et al. Ford multi-AV seasonal dataset[J]. The International Journal of Robotics Research, 2020, 39(12): 1367–1376. doi: 10.1177/0278364920961451.
[55]	YANG Hongji, LU Xiufan, and ZHU Yingying. Cross-view geo-localization with layer-to-layer transformer[C]. Proceedings of the 35th International Conference on Neural Information Processing Systems, 2021: 2222. doi: 10.5555/3540261.3542483. (查阅网上资料,未找到出版地信息,请补充).
[56]	SHI Yujiao, YU Xin, CAMPBELL D, et al. Where am I looking at? Joint location and orientation estimation by cross-view matching[C]. Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 4063–4071. doi: 10.1109/CVPR42600.2020.00412.
[57]	REGMI K and SHAH M. Bridging the domain gap for ground-to-aerial image matching[C]. Proceedings of 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), 2019: 470–479. doi: 10.1109/ICCV.2019.00056.
[58]	CHENG Lei, WANG Teng, LI Jiawen, et al. Offset regression enhanced cross-view feature interaction for ground-to-aerial geo-localization[J]. IEEE Transactions on Intelligent Vehicles, 2025, 10(1): 205–216. doi: 10.1109/TIV.2024.3411098.
[59]	ZHANG Xiaohan, LI Xingyu, SULTANI W, et al. GeoDTR+: Toward generic cross-view geolocalization via geometric disentanglement[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12): 10419–10433. doi: 10.1109/TPAMI.2024.3443652.
[60]	LI Chaoran, YAN Chao, XIANG Xiaojia, et al. AMPLE: Automatic progressive learning for orientation unknown ground-to-aerial geo-localization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 5800115. doi: 10.1109/TGRS.2024.3517654.
[61]	SHUGAEV M, SEMENOV I, ASHLEY K, et al. ArcGeo: Localizing limited field-of-view images using cross-view matching[C]. Proceedings of 2024 IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, USA, 2024: 208–217. doi: 10.1109/WACV57701.2024.00028.
[62]	LI Chaoran, YAN Chao, XIANG Xiaojia, et al. HADGEO: Image based 3-DoF cross-view geo-localization with hard sample mining[C]. Proceedings of 2024 IEEE International Conference on Acoustics, Speech and Signal Processing, Seoul, Korea, Republic of, 2024: 3520–3524. doi: 10.1109/ICASSP48485.2024.10445839.
[63]	YE Junyan, LV Zhutao, LI Weijia, et al. Cross-view image geo-localization with panorama-BEV Co-retrieval network[C]. Proceedings of the 18th European Conference on Computer Vision, Milan, Italy, 2024: 74–90. doi: 10.1007/978-3-031-72913-3_5.
[64]	ZHU Yingying, YANG Hongji, LU Yuxin, et al. Simple, effective and general: A new backbone for cross-view image geo-localization[J]. arXiv: 2302.01572, 2023. doi: 10.48550/arXiv.2302.01572.(查阅网上资料,未能确认文献类型,请确认).
[65]	PARK J, SUNG C, LEE S, et al. Cross-view geo-localization via effective negative sampling[C]. Proceedings of the 2024 24th International Conference on Control, Automation and Systems, Jeju, Korea, Republic of, 2024: 1078–1083. doi: 10.23919/ICCAS63016.2024.10773330.
[66]	ZHANG Xiaohan, SULTANI W, and WSHAH S. Cross-view image sequence geo-localization[C]. Proceedings of 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, USA, 2023: 2913–2922. doi: 10.1109/WACV56688.2023.00293.
[67]	SHI Yujiao and LI Hongdong. Beyond cross-view image retrieval: Highly accurate vehicle localization using satellite image[C]. Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 16989–16999. doi: 10.1109/CVPR52688.2022.01650.
[68]	LENTSCH T, XIA Zimin, CAESAR H, et al. SliceMatch: Geometry-guided aggregation for cross-view pose estimation[C]. Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023: 17225–17234. doi: 10.1109/CVPR52729.2023.01652.
[69]	XIA Zimin, BOOIJ O, and KOOIJ J F P. Convolutional cross-view pose estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(5): 3813–3831. doi: 10.1109/TPAMI.2023.3346924.
[70]	WANG Xiaolong, XU Runsen, CUI Zuofan, et al. Fine-grained cross-view geo-localization using a correlation-aware homography estimator[C]. Proceedings of the 37th International Conference on Neural Information Processing Systems, New Orleans, USA, 2023: 234. doi: 10.5555/3666122.3666356.
[71]	WANG Qiwei, WU Shaoxun, and SHI Yujiao. BevSplat: Resolving height ambiguity via feature-based Gaussian primitives for weakly-supervised cross-view localization[J]. arXiv: 2502.09080, 2025. doi: 10.48550/arXiv.2502.09080.(查阅网上资料,未能确认文献类型,请确认).
[72]	HU Wenmiao, ZHANG Yichen, LIANG Yuxuan, et al. PetalView: Fine-grained location and orientation extraction of street-view images via cross-view local search[C]. Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, Canada, 2023: 56–66. doi: 10.1145/3581783.3612007.
[73]	TANG T Y, DE MARTINI D, WU Shangzhe, et al. Self-supervised localisation between range sensors and overhead imagery[C]. Proceedings of Robotics: Science and Systems, Corvalis, USA, 2020. doi: 10.15607/RSS.2020.XVI.057.
[74]	HU Di, ZHANG Kai, YUAN Xia, et al. Real-time road intersection detection in sparse point cloud based on augmented viewpoints beam model[J]. Sensors, 2023, 23(21): 8854. doi: 10.3390/s23218854.