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GuanfangliPh.D | Discipline: Post: |
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Graduate School: Wuhan University Research direction: Phone: Email: fangli.guan@hdu.edu.cn Address: |
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GuanfangliPh.D | Discipline: Post: |
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Graduate School: Wuhan University Research direction: Phone: Email: fangli.guan@hdu.edu.cn Address: |
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Mobile phone access |
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Dr. Fangli Guan graduated from Wuhan University in 2022 with a Ph.D. in Photogrammetry and Remote Sensing Engineering, and obtained a Ph.D. in Geomatics and Surveying from Ghent University, Belgium, in 2023. Dr. Guan's research interests include navigation and location-based services, urban geoinformatics, and air-space-ground multi-modal collaborative sensing and modeling. He is currently focused on AR navigation, robot localization and autonomous navigation, SLAM in degraded environments, and smart application development for UAVs. Dr. Guan is leading a project funded by the National Natural Science Foundation of China, a Youth Science Foundation of Zhejiang Province, an open fund from a national key laboratory, and a key open fund project from a provincial key laboratory. He has published over 30 journal/conference papers, including 9 in CCF-A recommended conferences and top-tier journals, and has applied for more than 10 invention patents, with 4 already granted. Dr. Guan also serves as a reviewer for over ten internationally renowned journals, including top SCI journals such as ISPRS P&RS, Information Fusion, KBS, ESWA, and JAG, as well as top SSCI journals such as Cities and CEUS. Longitudinal research
Transverse scientific research
Publications
[1] Guan, F., Zhao, N., Haosheng, Wang. H, Fang, Z., Jiang, L., Zhang, J., Yu, Y., & Huang, H. (2025). Transformer Framework with Gradient-Aware Weighting Feature Alignment for Robust Cross-View Geo-Localization. Information Fusion. [2] Guan, F., Zhao, N., Fang, Z., Jiang, L., Zhang, J., Yu, Y., & Huang, H. (2025). Multi-level representation learning via ConvNeXt-based network for unaligned cross-view matching. Geo-Spatial Information Science, 1–14. https://doi.org/10.1080/10095020.2024.2439385 [3] Feng, W., Guan, F., Tu, J., & Xu, W. (2025). UV-AdaptFormer: adapting the segment anything model for urban village identification from high-resolution satellite imagery. Remote Sensing Letters, 16(6), 573-583. [4] Feng, W., Guan, F., Tu, J., & Xu, W. (2025). BuildingSAM: A Dual-Branch Feature-Augmented Segment Anything Model for Remote Sensing Building Extraction. IEEE Geoscience and Remote Sensing Letters. [5] Guan, F., Tang, K., Zhang, J., Bao, S., Chen, L., Chen, R., & Yu, Y. (2024). Autonomous wireless positioning system using crowdsourced Wi-Fi fingerprinting and self-detected FTM stations. Expert Systems with Applications, 255, 124566. [6] Guan, F., Liu, J., Zhang, J., Yan, L., & Jiang, L. (2024). Planar Reconstruction of Indoor Scenes from Sparse Views and Relative Camera Poses. Remote Sensing, 16(9), 1616. [7] Guan, F., Guan, Z., Zhang, J., Fang, Z., & Huang, H. (2024) Interactive effects of urban built environment on street crime risk: a multimodal data and lightweight machine learning combined approach. AsiaCarto2024, 2024. [8] Feng, W., Guan, F., Tu, J., Sun, C., & Xu, W. (2024). Water-Adapter: adapting the segment anything model for surface water extraction in optical very-high-resolution remotely sensed imagery. Remote Sensing Letters, 15(11), 1132-1142. [9] Feng, W., Guan, F., Sun, C., & Xu, W. (2024). Road-sam: Adapting the segment anything model to road extraction from large very-high-resolution optical remote sensing images. IEEE Geoscience and Remote Sensing Letters, 21, 1-5. [10] Feng, W., Guan, F., Sun, C., & Xu, W. (2024). Feature-Differencing-Based Self-Supervised Pre-Training for Land-Use/Land-Cover Change Detection in High-Resolution Remote Sensing Images. Land, 13(7), 927. [11] Feng, W., Guan, F., Sun, C., & Xu, W. (2024). Cross-modal change detection flood extraction based on self-supervised contrastive pre-training. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10, 75-82. [12] Chen, G+., Guan, F*., & Zhang, J. (2024, October). Enhanced YOLOv8 by integrating MSFF and MSAFM for UAV-Perspective Small Object Detection. In 2024 International Conference on Cyberworlds (CW) (pp. 332-333). IEEE. [13] Hu, Z+., Wang, H., Rong, X., Zhang, D., Xu, Z., & Guan, F*. (2024, October). Integrating Depth-Anything-V2 Depth Estimation and Sobel Operator Matrix for UAV Landing-site detection. In 2024 International Conference on Cyberworlds (CW) (pp. 342-343). IEEE. [14] Zhao, N+., Wang, H., Zhang, D., Rong, X., & Guan, F*. (2024, October). A ConvNeXt-based Spatial-Enhanced Attention and Convolution Combination for Cross-View Geo-Localization. In 2024 International Conference on Cyberworlds (CW) (pp. 387-388). IEEE. [15] Guan, F., Fang, Z., Zhang, X., Zhong, H., Zhang, J., & Huang, H. (2023). Using street-view panoramas to model the decision-making complexity of road intersections based on the passing branches during navigation. Computers, Environment and Urban Systems, 103, 101975. [16] Wei, X., Guan, F., Zhang, X., Van de Weghe, N., & Huang, H. (2023). Integrating planar and vertical environmental features for modelling land surface temperature based on street view images and land cover data. Building and Environment, 235, 110231. [17] Feng, W., Guan, F., Tu, J., Sun, C., & Xu, W. (2023). Detection of Changes in Buildings in Remote Sensing Images via Self-Supervised Contrastive Pre-Training and Historical Geographic Information System Vector Maps. Remote Sensing, 15(24), 5670. [18] Zhang, X., Liu, X., Chen, K., Guan, F., Luo, M., & Huang, H. (2023). Inferring building function: A novel geo-aware neural network supporting building-level function classification. Sustainable Cities and Society, 89, 104349. [19] Guan, F., Fang, Z., Wang, L., Zhang, X., Zhong, H., & Huang, H. (2022). Modelling people’s perceived scene complexity of real-world environments using street-view panoramas and open geodata. ISPRS Journal of Photogrammetry and Remote Sensing, 186, 315-331. [20] Guan, F., Fang, Z., Huang, H. (2022) A novel framework for modelling people’s perceived scene complexity of navigation environments based on street-level imagery and open geodata. Abstracts of the ICA, 2022, 5: 1-2. [21] Zhang, X., Sun, Y., Guan, F., Chen, K., Witlox, F., & Huang, H. (2022). Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale. Transportation Research Part C: Emerging Technologies, 143, 103854. [22] Fang, Z., Wang, L., Yang, F., & Guan, F. (2022). Landmark selection preferences of young students under orientation task within street environment. Journal of Location Based Services, 16(4), 245-287. [23] Guan, F., Fang, Z., Huang, H. (2021) Characterization and modeling of the decision-making complexity for navigation road intersections. 16th International Conference on Location Based Services. 2021: 5-9. [24] XU, H., WANG, L., FANG, Z., HE, M., HOU, X., ZUO, L., ... & NADIRE, A. (2021). Street-Facing Architectural Image Mapping and Architectural Style Map Generation Method Using Street View Images. Geomatics and Information Science of Wuhan University, 46(5), 659-671. [25] 方志祥, 姜宇昕, & 管昉立. (2021). 融合可视与不可视地标的行人相对定位方法. 武汉大学学报 (信息科学版), 46(5), 601-609. [26] Guan, F., Fang, Z., Yu, T., Feng, M., & Yang, F. (2020). Detecting visually salient scene areas and deriving their relative spatial relations from continuous street-view panoramas. International Journal of Digital Earth, 13(12), 1504–1531. https://doi.org/10.1080/17538947.2020.1731618 [27] Fang, Z., Yang, F., Guan, F., Feng, M., & Jiang, Y. (2020). A data model for organizing relative semantics as images to support pedestrian navigation computations. Transactions in GIS, 24(6), 1655-1680. [28] Wu, X. M., Guan, F. L., & Xu, A. J. (2020). Passive ranging based on planar homography in a monocular vision system. Journal of Information Processing Systems, 16(1), 155-170. [29] Yang, F., Fang, Z., & Guan, F. (2020, October). What Do We Actually Need During Self-localization in an Augmented Environment?. In International symposium on web and wireless geographical information systems (pp. 24-32). Cham: Springer International Publishing. [30] YANG, T., GUAN, F., & XU, A. (2018). Multiple trees contour extraction method based on Graph Cut algorithm. JOURNAL OF NANJING FORESTRY UNIVERSITY, 42(06), 91.
Books
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