Fang Liu

4.2k total citations
150 papers, 3.1k citations indexed

About

Fang Liu is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Fang Liu has authored 150 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 63 papers in Media Technology, 56 papers in Computer Vision and Pattern Recognition and 31 papers in Artificial Intelligence. Recurrent topics in Fang Liu's work include Remote-Sensing Image Classification (60 papers), Remote Sensing and Land Use (30 papers) and Advanced Image and Video Retrieval Techniques (27 papers). Fang Liu is often cited by papers focused on Remote-Sensing Image Classification (60 papers), Remote Sensing and Land Use (30 papers) and Advanced Image and Video Retrieval Techniques (27 papers). Fang Liu collaborates with scholars based in China, Hong Kong and United States. Fang Liu's co-authors include Licheng Jiao, Xiangrong Zhang, Xu Tang, Jingjing Ma, Maoguo Gong, Shuyuan Yang, Biao Hou, Liang Xiao, Jia Liu and Peng Zhao and has published in prestigious journals such as Journal of Applied Physics, The Science of The Total Environment and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Fang Liu

133 papers receiving 3.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Fang Liu China 30 1.6k 1.1k 646 513 475 150 3.1k
Hongqi Wang China 26 1.4k 0.9× 1.8k 1.6× 443 0.7× 516 1.0× 395 0.8× 107 3.0k
Xu Sun China 33 1.6k 1.0× 877 0.8× 1.0k 1.6× 336 0.7× 524 1.1× 232 4.2k
Xu Liu China 31 875 0.6× 1.3k 1.2× 259 0.4× 443 0.9× 659 1.4× 224 3.0k
Mengmeng Zhang China 30 3.0k 1.9× 1.3k 1.2× 1.4k 2.1× 351 0.7× 747 1.6× 164 4.5k
Qian Zhang China 28 1.0k 0.7× 1.6k 1.4× 399 0.6× 274 0.5× 485 1.0× 175 3.8k
Jiaqi Zhao China 29 862 0.6× 1.4k 1.2× 351 0.5× 297 0.6× 583 1.2× 227 3.4k
Wei He China 38 3.2k 2.1× 3.1k 2.9× 619 1.0× 247 0.5× 270 0.6× 184 5.3k
Chen Wu China 35 2.7k 1.7× 1.7k 1.6× 1.7k 2.6× 434 0.8× 794 1.7× 164 5.2k
Heng-Chao Li China 35 3.7k 2.4× 1.8k 1.7× 1.9k 3.0× 887 1.7× 760 1.6× 199 6.1k
Feng Gao China 26 1.4k 0.9× 1.1k 1.0× 704 1.1× 428 0.8× 364 0.8× 194 3.0k

Countries citing papers authored by Fang Liu

Since Specialization
Citations

This map shows the geographic impact of Fang Liu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fang Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fang Liu more than expected).

Fields of papers citing papers by Fang Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fang Liu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fang Liu. The network helps show where Fang Liu may publish in the future.

Co-authorship network of co-authors of Fang Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Fang Liu. A scholar is included among the top collaborators of Fang Liu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fang Liu. Fang Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Liu, Jia, Wenhua Zhang, Fang Liu, et al.. (2025). SIFANet: Spatial-Temporal Interaction and Frequency Adaptive Awareness Network for Change Detection in Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18. 6654–6667.
2.
Liu, Jia, et al.. (2025). Multiscale Self-Supervised Constraints and Change-Masks-Guided Network for Weakly Supervised Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–15. 2 indexed citations
3.
Liu, Fang, et al.. (2025). Text-Driven Adaptive Semantic Alignment Network for Cross-Scene Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–15.
4.
Liu, Jia, et al.. (2024). Stair Fusion Network With Context-Refined Attention for Remote Sensing Image Semantic Segmentation. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–17. 13 indexed citations
5.
Liu, Fang, et al.. (2024). Candidate-Aware and Change-Guided Learning for Remote Sensing Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–19. 4 indexed citations
6.
Liu, Fang, et al.. (2024). Difference Guidance Learning With Feature Alignment for Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–15.
7.
Jiao, Licheng, et al.. (2024). Multiplane Prior Guided Few-Shot Aerial Scene Rendering. 5009–5019. 1 indexed citations
8.
Liu, Jia, et al.. (2023). MANet: An Efficient Multidimensional Attention-Aggregated Network for Remote Sensing Image Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 61. 1–18. 19 indexed citations
9.
Liu, Fang, et al.. (2023). Adaptive Spatial and Difference Learning for Change Detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 16. 7447–7461. 5 indexed citations
10.
Zhang, Wenhua, et al.. (2022). Joint Variation Learning of Fusion and Difference Features for Change Detection in Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 60. 1–18. 23 indexed citations
11.
Li, Cuicui & Fang Liu. (2022). Large solutions of a class of degenerate equations associated with infinity Laplacian. Advanced Nonlinear Studies. 22(1). 67–87. 4 indexed citations
12.
Liu, Jia, Wenhua Zhang, Fang Liu, & Liang Xiao. (2021). A Probabilistic Model Based on Bipartite Convolutional Neural Network for Unsupervised Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 60. 1–14. 33 indexed citations
13.
Liu, Fang & Xiaoping Yang. (2021). A weighted eigenvalue problem of the biased infinity Laplacian *. Nonlinearity. 34(2). 1197–1237. 2 indexed citations
14.
Liu, Jia, Maoguo Gong, Liang Xiao, Wenhua Zhang, & Fang Liu. (2020). Evolving Connections in Group of Neurons for Robust Learning. IEEE Transactions on Cybernetics. 52(5). 3069–3082. 10 indexed citations
15.
McShea, William J., Xiaoli Shen, Fang Liu, et al.. (2020). China’s wildlife camera-trap monitoring needs a unified standard. Biodiversity Science. 28(9). 1125–1131. 3 indexed citations
16.
Li, Jing, et al.. (2020). Dynamic change detection method of vector result data in mine remote sensing monitoring. Guotu ziyuan yaogan. 32(3). 240–246.
17.
Liu, Fang, Licheng Jiao, Xu Tang, et al.. (2018). Local Restricted Convolutional Neural Network for Change Detection in Polarimetric SAR Images. IEEE Transactions on Neural Networks and Learning Systems. 30(3). 818–833. 88 indexed citations
18.
Liu, Fang, Andrew Burton‐Jones, & Dongming Xu. (2014). RUMORS ON SOCIAL MEDIA IN DISASTERS: EXTENDING TRANSMISSION TO RETRANSMISSION. Journal of the Association for Information Systems. 49. 39 indexed citations
19.
Liu, Fang, et al.. (2005). The Formation of Oxide Scale on FeCrAl at 900°C in dry O2 and O2 + 40% H2O. Chalmers Publication Library (Chalmers University of Technology). 3 indexed citations
20.
Liu, Fang & Junde Wang. (2004). Using Genetic Algorithm to Identify Completely Unknown System in FTIR Spectra Analysis. Journal of Environmental Science and Health Part A. 39(6). 1525–1533. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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