World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
13728
World Ranking
4042
National Ranking
534

Kun Fu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Kun Fu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 258 publications — 65th percentile

65% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Kun Fu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Kun Fu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 56 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Kun Fu is affiliated with the University of Chinese Academy of Sciences in China. Their research spans multiple domains within computer science and engineering, focusing primarily on areas related to remote sensing and image analysis.

The scientist has contributed extensively to the fields of Computer Science and Engineering, with a significant emphasis on subfields such as Computer Vision and Pattern Recognition, Aerospace Engineering, Media Technology, Artificial Intelligence, and Civil and Structural Engineering.

Key topics covered in Kun Fu's work include:

  • Advanced Image and Video Retrieval Techniques
  • Advanced Neural Network Applications
  • Remote-Sensing Image Classification
  • Advanced SAR Imaging Techniques
  • Synthetic Aperture Radar (SAR) Applications and Techniques
  • Domain Adaptation and Few-Shot Learning
  • Remote Sensing and LiDAR Applications

Kun Fu has published papers in several prominent scientific venues, notably:

  • IEEE Transactions on Geoscience and Remote Sensing
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Remote Sensing
  • arXiv (Cornell University)
  • ISPRS Journal of Photogrammetry and Remote Sensing

Recent publications by Kun Fu or related closely to their research include:

  • "FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery," 2022, ISPRS Journal of Photogrammetry and Remote Sensing
  • "Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images," 2020, ISPRS Journal of Photogrammetry and Remote Sensing
  • "An Anchor-Free Method Based on Feature Balancing and Refinement Network for Multiscale Ship Detection in SAR Images," 2020, IEEE Transactions on Geoscience and Remote Sensing
  • "RingMo: A Remote Sensing Foundation Model With Masked Image Modeling," 2022, IEEE Transactions on Geoscience and Remote Sensing
  • "Research Progress on Few-Shot Learning for Remote Sensing Image Interpretation," 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Collaborations have been frequent with several researchers, reflecting ongoing partnerships in the research community. Frequent co-authors include:

  • Xian Sun
  • Wenhui Diao
  • Hongfeng Yu
  • Yingchao Feng
  • Wenkai Zhang

Best Publications

  • SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects

    Xue Yang;Jirui Yang;Junchi Yan;Yue Zhang

  • FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery

    Unknown

  • Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images

    Yang Zhan;Kun Fu;Menglong Yan;Xian Sun

  • Orientation robust object detection in aerial images using deep convolutional neural network

    Haigang Zhu;Xiaogang Chen;Weiqun Dai;Kun Fu

  • Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks

    Xue Yang;Hao Sun;Kun Fu;Jirui Yang

  • A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection

    Jiao Jiao;Yue Zhang;Hao Sun;Xue Yang

  • Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks

    Xue Yang;Hao Sun;Kun Fu;Jirui Yang

  • RingMo: A Remote Sensing Foundation Model With Masked Image Modeling

    Unknown

  • FMSSD: Feature-Merged Single-Shot Detection for Multiscale Objects in Large-Scale Remote Sensing Imagery

    Peijin Wang;Xian Sun;Wenhui Diao;Kun Fu

  • SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects

    Xue Yang;Jirui Yang;Junchi Yan;Yue Zhang

  • Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images

    Kun Fu;Zhonghan Chang;Yue Zhang;Guangluan Xu

  • Multi-task Representation Learning for Travel Time Estimation

    Yaguang Li;Kun Fu;Zheng Wang;Cyrus Shahabi

  • An Anchor-Free Method Based on Feature Balancing and Refinement Network for Multiscale Ship Detection in SAR Images

    Jiamei Fu;Xian Sun;Zhirui Wang;Kun Fu

  • BFSIFT: A Novel Method to Find Feature Matches for SAR Image Registration

    Shanhu Wang;Hongjian You;Kun Fu

  • MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

    Daoyu Lin;Kun Fu;Yang Wang;Guangluan Xu

  • Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images

    Ruigang Niu;Xian Sun;Yu Tian;Wenhui Diao

  • Efficient Saliency-Based Object Detection in Remote Sensing Images Using Deep Belief Networks

    Wenhui Diao;Xian Sun;Xinwei Zheng;Fangzheng Dou

  • Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers

    Zhibin Xiao;Yang Wang;Kun Fu;Fan Wu

  • Research Progress on Few-Shot Learning for Remote Sensing Image Interpretation

    Xian Sun;Bing Wang;Zhirui Wang;Hao Li

  • Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information

    Unknown

  • Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval

    Zhiqiang Yuan;Wenkai Zhang;Kun Fu;Xuan Li

  • Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network

    Xue Yang;Hao Sun;Xian Sun;Menglong Yan

  • A New Method on Inshore Ship Detection in High-Resolution Satellite Images Using Shape and Context Information

    Ge Liu;Yasen Zhang;Xinwei Zheng;Xian Sun

  • Object Detection in High-Resolution Remote Sensing Images Using Rotation Invariant Parts Based Model

    Wanceng Zhang;Xian Sun;Kun Fu;Chenyuan Wang

  • Automatic Water-Body Segmentation From High-Resolution Satellite Images via Deep Networks

    Ziming Miao;Kun Fu;Hao Sun;Xian Sun

Frequent Co-Authors

Xian Sun
Xian Sun University of Chinese Academy of Sciences
Wenhui Diao
Wenhui Diao Chinese Academy of Sciences
Weiya Zhang
Weiya Zhang University of Nottingham
Xue Yang
Xue Yang Shanghai Jiao Tong University
Heng-Chao Li
Heng-Chao Li Southwest Jiaotong University
William J. Emery
William J. Emery University of Colorado Boulder
Cheng Wang
Cheng Wang Xiamen University
Stefan Hinz
Stefan Hinz Karlsruhe Institute of Technology
Qixiang Ye
Qixiang Ye Chinese Academy of Sciences
Junchi Yan
Junchi Yan Shanghai Jiao Tong University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Computer Science in the USA opens numerous doors, but many students also consider related fields to expand their career opportunities. Online degrees can be a flexible and affordable way to specialize or cross-train in areas closely linked to Computer Science.

For those interested in applying technology to environmental challenges, an environmental engineering online degree is a strong option. This pathway blends science, computation, and engineering for roles in sustainability and green tech.

If you want to boost your technical credentials without breaking the bank, consider the cheapest online master’s mechanical engineering programs. Mechanical engineering skills are in demand across robotics, AI, and manufacturing sectors.

For students fascinated by the fundamentals of the universe, a physics degree online can complement computational studies and unlock roles in research or technology development.

Lastly, aspiring data professionals can explore options for a data scientist degree. Data science is a top career path for computer science graduates looking to enter analytics, AI, or big data fields.

Best Scientists Citing Kun Fu

Trending Scientists

Recently Published Articles