Awesome Geo-localization
News
Recently, we raise a special issue on Remote Sensing (IF=5.349) from now to 16 June 2023. You are welcomed to submit your manuscript at (https://www.mdpi.com/journal/remotesensing/special_issues/EMPK490239), but you need to keep open-source fee in mind.
University-1652 Dataset
Drone <-> Satellite
| Methods | R@1 | AP | R@1 | AP | Reference |
|---|---|---|---|---|---|
| Drone -> Satellite | Satellite -> Drone | ||||
| Contrastive Loss | 52.39 | 57.44 | 63.91 | 52.24 | |
| Triplet Loss (margin=0.3) | 55.18 | 59.97 | 63.62 | 53.85 | |
| Triplet Loss (margin=0.5) | 53.58 | 58.60 | 64.48 | 53.15 | |
| Weighted Soft Margin Triplet Loss | 53.21 | 58.03 | 65.62 | 54.47 | Liu L, Li H. Lending orientation to neural networks for cross-view geo-localization[C]. CVPR, 2019: 5624-5633. [Paper] |
| Instance Loss | 58.23 | 62.91 | 74.47 | 59.45 | Zheng Z, Zheng L, Garrett M, et al. Dual-Path Convolutional Image-Text Embedding with Instance Loss. TOMM 2020. [Paper] |
| Instance Loss + Verification Loss | 61.30 | 65.68 | 75.04 | 62.87 | Zheng Z, Zheng L, Yang Y. A discriminatively learned cnn embedding for person reidentification[J]. TOMM, 2017, 14(1): 1-20. [Paper] [Code] |
| Instance Loss + GeM Pooling | 65.32 | 69.61 | 79.03 | 65.35 | Radenović, Filip, Giorgos Tolias, and Ondřej Chum. “Fine-tuning CNN image retrieval with no human annotation.” TPAMI (2018): 1655-1668. |
| Instance Loss + Weighted Soft Margin Triplet Loss | 65.93 | 70.18 | 76.03 | 66.36 | |
| RK-Net (USAM) | 66.13 | 70.23 | 80.17 | 65.76 | Lin J, Zheng Z, Zhong Z, Luo Z, Li S, Yang Y, Sebe N. Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization. TIP 2022. [Paper] [Code] |
| LCM (ResNet-50) | 66.65 | 70.82 | 79.89 | 65.38 | Ding L, Zhou J, Meng L, et al. A Practical Cross-View Image Matching Method between UAV and Satellite for UAV-Based Geo-Localization[J]. Remote Sensing, 2021, 13(1): 47. [Paper] |
| DWDR | 69.77 | 73.73 | 81.46 | 70.45 | Tingyu W, Zhedong Z, Zunjie Z, Yuhan G, Yi Y, and Chenggang Y. “Learning Cross-view Geo-localization Embeddings via Dynamic Weighted Decorrelation Regularization” arXiv 2022. [Paper] |
| Instance Loss + GNN ReRanking | 70.30 | 74.11 | - | - | Zhang, Xuanmeng, Minyue Jiang, Zhedong Zheng, Xiao Tan, Errui Ding, and Yi Yang. “Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective.” arXiv 2020. [Paper][Code] |
| Instance Loss + USAM + SAFA | 72.19 | 75.79 | 83.23 | 71.77 | |
| MuSe-Net (Normal Weather) | 74.48 | 77.83 | 88.02 | 75.10 | Wang T, Zheng Z, Sun Y, et al. Multiple-environment Self-adaptive Network for Aerial-view Geo-localization[J]. arXiv preprint arXiv:2204.08381, 2022. |
| LPN | 75.93 | 79.14 | 86.45 | 74.79 | Tingyu W, Zhedong Z, Chenggang Y, and Yi Y. Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization. TCSVT 2021. [Paper] [Code] |
| LPN + CA-HRS | 76.67 | 79.77 | 86.88 | 74.84 | Zeng Lu, Tao Pu, Tianshui Chen, and Liang Lin. Content-Aware Hierarchical Representation Selection for Cross-View Geo-Localization ACCV2022. [Paper] [Code] |
| Instance Loss + Weighted Soft Margin Triplet Loss + LPN | 76.29 | 79.46 | 81.74 | 73.58 | |
| Instance Loss + Verification Loss + LPN | 77.08 | 80.18 | 85.02 | 73.80 | |
| Instance Loss + USAM + LPN | 77.60 | 80.55 | 86.59 | 75.96 | Lin J, Zheng Z, Zhong Z, Luo Z, Li S, Yang Y, Sebe N. Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization. TIP 2022. [Paper] [Code] |
| F3Net | 78.64 | 81.60 | - | - | Bo Sun, Ganchao Liu and Yuan Yuan. F3-Net: Multiview Scene Matching for Drone-Based Geo-Localization. TGRS 2023. |
| SAIG-D | 78.85 | 81.62 | 86.45 | 78.48 | Yingying Zhu, Hongji Yang, Yuxin Lu and Qiang Huang. Simple, Effective and General: A New Backbone for Cross-view Image Geo-localization. ArXiv 2023 |
| LDRVSD | 78.66 | 81.55 | 89.30 | 79.17 | Qian Hu, Wansi Li, Xing Xu, Ning Liu, Lei Wang. Learning discriminative representations via variational self-distillation for cross-view geo-localization. Computers and Electrical Engineering 2022 [Paper] |
| PCL | 79.47 | 83.63 | 87.69 | 78.51 | Xiaoyang Tian, Jie Shao, Deqiang Ouyang, and Heng Tao Shen. UAV-Satellite View Synthesis for Cross-view Geo-Localization. TCSVT 2021. [Paper] |
| LPN + DWDR | 81.51 | 84.11 | 88.30 | 79.38 | Tingyu W, Zhedong Z, Zunjie Z, Yuhan G, Yi Y, and Chenggang Y. “Learning Cross-view Geo-localization Embeddings via Dynamic Weighted Decorrelation Regularization” arXiv 2022. [Paper] |
| FSRA (k=1) | 82.25 | 84.82 | 87.87 | 81.53 | Ming Dai, Jianhong Hu, Jiedong Zhuang, Enhui Zheng. A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization. TCSVT 2022. [Paper] [Code] |
| FSRA (k=3) | 84.51 | 86.71 | 88.45 | 83.37 | |
| PAAN | 84.51 | 86.78 | 91.01 | 82.28 | Duc Viet Bui, Masao Kubo, Hiroshi Sato. A Part-aware Attention Neural Network for Cross-view Geo-localization between UAV and Satellite. Journal of Robotics Networking and Artificial Life 2022 [Paper] |
| Swin-B + DWDR | 86.41 | 88.41 | 91.30 | 86.02 | Tingyu W, Zhedong Z, Zunjie Z, Yuhan G, Yi Y, and Chenggang Y. “Learning Cross-view Geo-localization Embeddings via Dynamic Weighted Decorrelation Regularization” arXiv 2022. [Paper] |
| MBF | 89.05 | 90.61 | 93.15 | 88.17 | Runzhe Zhu , Mingze Yang , Ling Yin * , Fei Wu and Yuncheng Yang. “UAV’s Status Is Worth Considering: A Fusion Representations Matching Method for Geo-Localization” Sensors |
| MCCG | 89.64 | 91.32 | 94.30 | 89.39 | Tianrui Shen, Yingmei Wei, Lai Kang, Shanshan Wan and Yee-Hong Yang. MCCG: A ConvNeXt-based Multiple-Classifier Method for Cross-view Geo-localization. TCSVT 2023 [Code] |
Ground <-> Satellite
| Methods | Training Set | R@1 | AP | R@1 | AP | Reference |
|---|---|---|---|---|---|---|
| Ground -> Satellite | Satellite -> Ground | |||||
| Instance Loss | Satellite + Ground | 0.62 | 1.60 | 0.86 | 1.00 | Zheng Z, Zheng L, Garrett M, et al. Dual-Path Convolutional Image-Text Embedding with Instance Loss. TOMM 2020. [Paper] |
| Instance Loss | Satellite + Drone + Ground | 1.28 | 2.29 | 1.57 | 1.52 | |
| Instance Loss | Satellite + Drone + Ground + Google Image | 1.20 | 2.52 | 1.14 | 1.41 | |
| LPN | Satellite + Ground | 0.74 | 1.83 | 1.43 | 1.31 | Tingyu W, Zhedong Z, Chenggang Y, and Yi Y. Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization. TCSVT 2021. [Paper] [Code] |
| LPN | Satellite + Drone + Ground | 0.81 | 2.21 | 1.85 | 1.66 | Tingyu W, Zhedong Z, Chenggang Y, and Yi Y. Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization. TCSVT 2021. [Paper] [Code] |
| PCLD | Satellite + Drone + Ground | 9.15 | 14.16 | - | - | Zeng, Z., Wang, Z., Yang, F., & Satoh, S. I. (2022). Geo-Localization via Ground-to-Satellite Cross-View Image Retrieval. IEEE Transactions on Multimedia. [Paper] |
cvusa Dataset
| Methods | R@1 | R@5 | R@10 | R@Top1 | Reference |
|---|---|---|---|---|---|
| Workman | - | - | - | 34.40 | Scott Workman, Richard Souvenir, and Nathan Jacobs. ICCV 2015. Wide-area image geolocalization with aerial reference imagery [Paper] |
| Zhai | - | - | - | 43.20 | Menghua Zhai, Zachary Bessinger, Scott Workman, and Nathan Jacobs. CVPR 2017. Predicting ground-level scene layout from aerial imagery.[Paper] |
| Vo | - | - | - | 63.70 | Nam N Vo and James Hays. ECCV 2016. Localizing and orienting street views using overhead imagery |
| CVM-Net | 18.80 | 44.42 | 57.47 | 91.54 | Sixing Hu, Mengdan Feng, Rang MH Nguyen, and Gim Hee Lee. CVPR 2018. CVM-net:Cross-view matching network for image-based ground-to-aerial geo-localization. [Paper] |
| Orientation** | 27.15 | 54.66 | 67.54 | 93.91 | Liu Liu and Hongdong Li. CVPR 2019. Lending Orientation to Neural Networks for Cross-view Geo-localization [Paper] |
| Siam-FCANet | - | - | - | 98.3 | Sudong C, Yulan G, Salman K, et al. Ground-to-Aerial Image Geo-Localization With a Hard Exemplar Reweighting Triplet Loss. ICCV 2019. [Paper] |
| Feature Fusion | 48.75 | - | 81.27 | 95.98 | Krishna Regmi, Mubarak Shah, et al. Bridging the Domain Gap for Ground-to-Aerial Image Matching. ICCV 2019. [Paper] |
| Instance Loss | 43.91 | 66.38 | 74.58 | 91.78 | Zheng Z, Zheng L, Garrett M, et al. Dual-Path Convolutional Image-Text Embedding with Instance Loss. TOMM 2020. [Paper] [Code] |
| RK-Net (USAM) | 52.50 | - | - | 96.52 | Lin J, Zheng Z, Zhong Z, Luo Z, Li S, Yang Y, Sebe N. Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization. TIP 2022. [Paper] [Code] |
| CVFT | 61.43 | 84.69 | 90.49 | 99.02 | Shi Y, Yu X, Liu L, et al. Optimal Feature Transport for Cross-View Image Geo-Localization. AAAI 2020. [Paper] |
| DWDR | 75.62 | 90.45 | 93.60 | 98.60 | Tingyu W, Zhedong Z, Zunjie Z, Yuhan G, Yi Y, and Chenggang Y. “Learning Cross-view Geo-localization Embeddings via Dynamic Weighted Decorrelation Regularization” arXiv 2022. [Paper] |
| MS Attention w DataAug | 75.95 | 91.90 | 95.00 | 99.42 | Rodrigues, Royston, and Masahiro Tani. “Are These From the Same Place? Seeing the Unseen in Cross-View Image Geo-Localization.” WACV 2021. [Paper] |
| MuSe-Net (Normal Weather) | 78.04 | - | - | - | Wang T, Zheng Z, Sun Y, et al. Multiple-environment Self-adaptive Network for Aerial-view Geo-localization[J]. arXiv preprint arXiv:2204.08381, 2022. |
| LPN | 85.79 | 95.38 | 96.98 | 99.41 | Tingyu Wang, Zhedong Zheng, Chenggang Yan, and Yi, Yang. Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization. TCSVT 2021. [Paper] [Code] |
| LPN + CA-HRS | 87.16 | 95.98 | 97.55 | 99.49 | Zeng Lu, Tao Pu, Tianshui Chen, and Liang Lin. Content-Aware Hierarchical Representation Selection for Cross-View Geo-Localization ACCV2022. [Paper] [Code] |
| SAFA* | 89.84 | 96.93 | 98.14 | 99.64 | Yujiao Shi, Liu Liu, Xin Yu, et al. Spatial-Aware Feature Aggregation for Cross-View Image based Geo-Localization. NeurIPS 2019. [Paper] |
| SAFA* + USAM | 90.16 | - | - | 99.67 | Lin J, Zheng Z, Zhong Z, Luo Z, Li S, Yang Y, Sebe N. Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization. TIP 2022. [Paper] [Code] |
| LPN + USAM | 91.22 | - | - | 99.67 | Lin J, Zheng Z, Zhong Z, Luo Z, Li S, Yang Y, Sebe N. Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization. TIP 2022. [Paper] [Code] |
| DSM* | 91.96 | 97.50 | 98.54 | 99.67 | Yujiao Shi, Xin Yu, Dylan Campbell, and Hongdong Li. “Where am i looking at? joint location and orientation estimation by cross-view matching.” CVPR 2020. [Paper] [Code] |
| Toker etal.* | 92.56 | 97.55 | 98.33 | 99.67 | Aysim Toker, Qunjie Zhou, Maxim Maximov, Laura Leal-Taixé. Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization. CVPR 2021 [Paper] |
| Shi etal.* | 92.69 | 97.78 | 98.60 | 99.61 | Yujiao Shi, Xin Yu, Liu Liu, Dylan Campbell, Piotr Koniusz, and Hongdong Li. Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching. TPAMI 2022. [Paper] [Code] |
| SAFA* + LPN | 92.83 | 98.00 | 98.85 | 99.78 | Tingyu Wang, Zhedong Zheng, Chenggang Yan, and Yi, Yang. Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization. TCSVT 2021. [Paper] [Code] |
| SIRNet* | 93.74 | 98.02 | 98.85 | 99.76 | Xiufan Lu, Siqi Luo, Yingying Zhu. “It’s Okay to Be Wrong: Cross-View Geo-Localization With Step-Adaptive Iterative Refinement” IEEE Transactions on Geoscience and Remote Sensing 2022 [Paper] |
| Polar-L2LTR* | 94.05 | 98.27 | 98.99 | 99.67 | Hongji Yang, Xiufan Lu, Yingying Zhu. Cross-view Geo-localization with Layer-to-Layer Transformer. NeurIPS 2021 [Paper] [Code] |
| TransGeo | 94.08 | 98.36 | 99.04 | 99.77 | Sijie Zhu, Mubarak Shah, Chen Chen. TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization. CVPR 2022 [Paper] [Code] |
| MGTL* | 94.11 | 98.30 | 99.03 | 99.74 | Jianwei Zhao, Qiang Zhai, Rui Huang, Hong Cheng. Mutual Generative Transformer Learning for Cross-view Geo-localization [Paper] |
| GeoDTR | 93.76 | 98.47 | 99.22 | 99.85 | Xiaohan Zhang, Xingyu Li, Waqas Sultani, Yi Zhou, Safwan Wshah. Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence [Paper] [Code] |
| LPN* + DWDR | 94.33 | 98.54 | 99.09 | 99.80 | Tingyu W, Zhedong Z, Zunjie Z, Yuhan G, Yi Y, and Chenggang Y. “Learning Cross-view Geo-localization Embeddings via Dynamic Weighted Decorrelation Regularization” arXiv 2022. [Paper] |
| GeoDTR* | 95.43 | 98.86 | 99.34 | 99.86 | Xiaohan Zhang, Xingyu Li, Waqas Sultani, Yi Zhou, Safwan Wshah. Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence [Paper] [Code] |
| FI* | 95.50 | - | - | - | Wenmiao Hu, Yichen Zhang, Yuxuan Liang, Yifang Yin, Anderi Georgecu, An Tran, Hannes Kruppa, See-Kiong Ng, Roger Zimmermann. Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery. ACM MM 2022 [Paper] |
| SAIG-D* | 96.34 | 99.10 | 99.50 | 99.86 | Yingying Zhu, Hongji Yang, Yuxin Lu and Qiang Huang. Simple, Effective and General: A New Backbone for Cross-view Image Geo-localization. ArXiv 2023 |
| *: The method utilizes the polar transformation (assuming that all satellite images face north) as input. | |||||
| ** : The method utilizes the polar prior hint. |
cvact val Dataset
| Methods | R@1 | R@5 | R@10 | R@Top1 | Reference |
|---|---|---|---|---|---|
| CVM-Net | 20.15 | 45.00 | 56.87 | 87.57 | Sixing Hu, Mengdan Feng, Rang MH Nguyen, and Gim Hee Lee. CVPR 2018. CVM-net:Cross-view matching network for image-based ground-to-aerial geo-localization. [Paper] |
| Instance Loss | 31.20 | 53.64 | 63.00 | 85.27 | Zheng Z, Zheng L, Garrett M, et al. Dual-Path Convolutional Image-Text Embedding with Instance Loss. TOMM 2020. [Paper] [Code] |
| RK-Net (USAM) | 40.53 | - | - | 89.12 | Lin J, Zheng Z, Zhong Z, Luo Z, Li S, Yang Y, Sebe N. Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization. TIP 2022. [Paper] [Code] |
| Orientation** | 46.96 | 68.28 | 75.48 | 92.04 | Liu Liu and Hongdong Li. CVPR 2019. Lending Orientation to Neural Networks for Cross-view Geo-localization [Paper] |
| CVFT | 61.05 | 81.33 | 86.52 | 95.93 | Shi Y, Yu X, Liu L, et al. Optimal Feature Transport for Cross-View Image Geo-Localization. AAAI 2020. [Paper] |
| DWDR | 66.76 | 83.34 | 87.11 | 95.10 | Tingyu W, Zhedong Z, Zunjie Z, Yuhan G, Yi Y, and Chenggang Y. “Learning Cross-view Geo-localization Embeddings via Dynamic Weighted Decorrelation Regularization” arXiv 2022. [Paper] |
| MS Attention w DataAug | 73.19 | 90.39 | 93.38 | 97.45 | Rodrigues, Royston, and Masahiro Tani. “Are These From the Same Place? Seeing the Unseen in Cross-View Image Geo-Localization.” WACV 2021. [Paper] |
| LPN | 79.99 | 90.63 | 92.56 | 97.03 | Tingyu Wang, Zhedong Zheng, Chenggang Yan, and Yi, Yang. Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization. TCSVT 2021. [Paper] [Code] |
| LPN + CA-HRS | 80.91 | 90.95 | 92.93 | 97.07 | Zeng Lu, Tao Pu, Tianshui Chen, and Liang Lin. Content-Aware Hierarchical Representation Selection for Cross-View Geo-Localization ACCV2022. [Paper] [Code] |
| LDRVSD | 80.98 | 91.48 | 93.33 | - | Qian Hu, Wansi Li, Xing Xu, Ning Liu, Lei Wang. Learning discriminative representations via variational self-distillation for cross-view geo-localization. Computers and Electrical Engineering 2022 |
| SAFA* | 81.03 | 92.80 | 94.84 | 98.17 | Yujiao Shi, Liu Liu, Xin Yu, et al. Spatial-Aware Feature Aggregation for Cross-View Image based Geo-Localization. NeurIPS 2019. [Paper] |
| LPN + USAM | 82.02 | - | - | 98.18 | Lin J, Zheng Z, Zhong Z, Luo Z, Li S, Yang Y, Sebe N. Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization. TIP 2022. [Paper] [Code] |
| SAFA* + USAM | 82.40 | - | - | 98.00 | Lin J, Zheng Z, Zhong Z, Luo Z, Li S, Yang Y, Sebe N. Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization. TIP 2022. [Paper] [Code] |
| DSM* | 82.49 | 92.44 | 93.99 | 97.32 | Yujiao Shi, Xin Yu, Dylan Campbell, and Hongdong Li. “Where am i looking at? joint location and orientation estimation by cross-view matching.” CVPR 2020. [Paper] [Code] |
| Shi etal.* | 82.70 | 92.50 | 94.24 | 97.65 | Yujiao Shi, Xin Yu, Liu Liu, Dylan Campbell, Piotr Koniusz, and Hongdong Li. Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching. TPAMI 2022. [Paper] [Code] |
| Toker etal.* | 83.28 | 93.57 | 95.42 | 98.22 | Aysim Toker, Qunjie Zhou, Maxim Maximov, Laura Leal-Taixé. Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization. CVPR 2021 [Paper] |
| SAFA* + LPN | 83.66 | 94.14 | 95.92 | 98.41 | Tingyu Wang, Zhedong Zheng, Chenggang Yan, and Yi, Yang. Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization. TCSVT 2021. [Paper] [Code] |
| LPN* + DWDR | 83.73 | 92.78 | 94.53 | 97.78 | Tingyu W, Zhedong Z, Zunjie Z, Yuhan G, Yi Y, and Chenggang Y. “Learning Cross-view Geo-localization Embeddings via Dynamic Weighted Decorrelation Regularization” arXiv 2022. [Paper] |
| Polar-L2LTR* | 84.89 | 94.59 | 95.96 | 98.37 | Hongji Yang, Xiufan Lu, Yingying Zhu. Cross-view Geo-localization with Layer-to-Layer Transformer. NeurIPS 2021 [Paper] [Code] |
| TransGeo | 84.95 | 94.14 | 95.78 | 98.37 | Sijie Zhu, Mubarak Shah, Chen Chen. TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization. CVPR 2022 [Paper] [Code] |
| MGTL* | 85.35 | 94.45 | 96.06 | 98.48 | Jianwei Zhao, Qiang Zhai, Rui Huang, Hong Cheng. Mutual Generative Transformer Learning for Cross-view Geo-localization [Paper] |
| SIRNet* | 86.02 | 94.45 | 96.02 | 98.33 | Xiufan Lu, Siqi Luo, Yingying Zhu. “It’s Okay to Be Wrong: Cross-View Geo-Localization With Step-Adaptive Iterative Refinement” IEEE Transactions on Geoscience and Remote Sensing 2022 [Paper] |
| GeoDTR | 85.43 | 94.81 | 96.11 | 98.26 | Xiaohan Zhang, Xingyu Li, Waqas Sultani, Yi Zhou, Safwan Wshah. Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence [Paper] [Code] |
| GeoDTR* | 86.21 | 95.44 | 96.72 | 98.77 | Xiaohan Zhang, Xingyu Li, Waqas Sultani, Yi Zhou, Safwan Wshah. Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence [Paper] [Code] |
| FI* | 86.79 | - | - | - | Wenmiao Hu, Yichen Zhang, Yuxuan Liang, Yifang Yin, Anderi Georgecu, An Tran, Hannes Kruppa, See-Kiong Ng, Roger Zimmermann. Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery. ACM MM 2022 [Paper] |
| SAIG-D* | 89.06 | 96.11 | 97.08 | 98.89 | Yingying Zhu, Hongji Yang, Yuxin Lu and Qiang Huang. Simple, Effective and General: A New Backbone for Cross-view Image Geo-localization. ArXiv 2023 |
| *: The method utilizes the polar transformation (assuming that all satellite images face north) as input. | |||||
| ** : The method utilizes the polar prior hint. |