Ling Tan
Impact in
- Media Technology top 5%
- Advanced Image Fusion Techniques
- Remote-Sensing Image Classification
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- Advanced Neural Network Applications
Papers in
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- Advanced Image Fusion Techniques 5
- Remote-Sensing Image Classification 3
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- Optical Systems and Laser Technology 2
- Co-authors
- Jingming Xia (17 shared papers)Bin Li (1 shared paper)Yufeng Liu (1 shared paper)Heng Pan (1 shared paper)Yan Zhang (1 shared paper)Ying Liang (2 shared papers)Yue Wang (1 shared paper)Hui Wu (1 shared paper)
- Journals
- IEEE Geoscience and Remote Sensing Letters (2 papers)IEEE Access (2 papers)International Journal of Embedded Systems (2 papers)Engineering Applications of Artificial Intelligence (1 paper)Applied Sciences (1 paper)
- Partner nations
- ChinaUnited StatesNorway
In The Last Decade
Ling Tan
26 papers receiving 307 citations
Peers
Comparison fields: 5 of 60
- Media Technology 63
- Computer Vision and Pattern Recognition 93
- Computer Networks and Communications 92
- Industrial and Manufacturing Engineering 39
- Neurology 25
Countries citing papers authored by Ling Tan
This map shows the geographic impact of Ling Tan'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 Ling Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ling Tan more than expected).
Fields of papers citing papers by Ling Tan
This network shows the impact of papers produced by Ling Tan. 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 Ling Tan. The network helps show where Ling Tan may publish in the future.
Co-authors
The 25 scholars most cited alongside Ling Tan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 115 | |
| 2 | 2020 | 24 | |
| 3 | 2021 | 23 | |
| 4 | 2019 | 22 | |
| 5 | 2023 | 18 | |
| 6 | 2024 | 13 | |
| 7 | 2023 | 12 | |
| 8 | 2024 | 11 | |
| 9 | 2024 | 11 | |
| 10 | 2021 | 11 | |
| 11 | 2024 | 10 | |
| 12 | 2020 | 7 | |
| 13 | 2023 | 5 | |
| 14 | 2024 | 5 | |
| 15 | 2023 | 4 | |
| 16 | 2021 | 3 | |
| 17 | 2024 | 3 | |
| 18 | 2021 | 3 | |
| 19 | 2025 | 3 | |
| 20 | 2020 | 3 |
About Ling Tan
Ling Tan is a scholar working on Media Technology, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering, having authored 30 papers that have together received 315 indexed citations. Recurring topics across this work include Advanced Image Fusion Techniques (5 papers), Remote Sensing and Land Use (4 papers), Photoacoustic and Ultrasonic Imaging (3 papers), UAV Applications and Optimization (3 papers), IoT and Edge/Fog Computing (3 papers), Remote-Sensing Image Classification (3 papers), AI in cancer detection (3 papers) and Optical Systems and Laser Technology (2 papers). The work is most often cited by research in Media Technology (63 citations), Computer Vision and Pattern Recognition (93 citations), Computer Networks and Communications (92 citations), Industrial and Manufacturing Engineering (39 citations) and Neurology (25 citations). Ling Tan has collaborated with scholars based in China, United States and Norway. Frequent co-authors include Jingming Xia, Bin Li, Yufeng Liu, Heng Pan, Yan Zhang, Ying Liang, Yue Wang, Yue Wang, Hui Wu and Zifeng Xu. Their work appears in journals such as IEEE Geoscience and Remote Sensing Letters, IEEE Access, International Journal of Embedded Systems, Engineering Applications of Artificial Intelligence and Applied Sciences.
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.