World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
60
Citations
14473
World Ranking
3238
National Ranking
434

Cheng Wang 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 Cheng Wang 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: 483 publications — 92nd percentile

92% of scientists in this discipline score the same or lower.

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

Cheng Wang 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 Cheng Wang 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: 60 D-Index — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Overview

Cheng Wang is a researcher affiliated with Xiamen University in China, with a focus primarily on Environmental Science. Their work spans several subfields including Environmental Engineering, Ecology, Global and Planetary Change, Geology, and Nature and Landscape Conservation.

The scientist's research topics encompass a range of areas within remote sensing and environmental monitoring. These include:

  • Remote Sensing and LiDAR Applications
  • Remote Sensing in Agriculture
  • 3D Surveying and Cultural Heritage
  • Forest Ecology and Management
  • Forest Management and Policy
  • Advanced Optical Sensing Technologies
  • Cryospheric Studies and Observations

Wang has published extensively in journals notable for remote sensing and earth observation studies. Common publication venues include:

  • Remote Sensing
  • Remote Sensing of Environment
  • Zenodo (CERN European Organization for Nuclear Research)
  • International Journal of Applied Earth Observation and Geoinformation
  • IEEE Geoscience and Remote Sensing Letters

Some of their recent published papers are:

  • A Noise Removal Algorithm Based on OPTICS for Photon-Counting LiDAR Data (2020) in IEEE Geoscience and Remote Sensing Letters
  • Retrieving building height in urban areas using ICESat-2 photon-counting LiDAR data (2021) in International Journal of Applied Earth Observation and Geoinformation
  • A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery (2020) in Remote Sensing
  • Mapping forest height using photon-counting LiDAR data and Landsat 8 OLI data: A case study in Virginia and North Carolina, USA (2020) in Ecological Indicators
  • Comprehensive LiDAR simulation with efficient physically-based DART-Lux model (I): Theory, novelty, and consistency validation (2022) in Remote Sensing of Environment

Wang has collaborated frequently with several coauthors, including:

  • Xiaohuan Xi
  • Sheng Nie
  • Xiaoxiao Zhu
  • Xuebo Yang
  • Jinliang Wang

Best Publications

  • GMAN: A Graph Multi-Attention Network for Traffic Prediction

    Chuanpan Zheng;Xiaoliang Fan;Cheng Wang;Jianzhong Qi

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

    Unknown

  • deep learning on 3D point clouds

    Unknown

  • Using mobile laser scanning data for automated extraction of road markings

    Haiyan Guan;Jonathan Li;Jonathan Li;Yongtao Yu;Cheng Wang

  • Environmental Epidemiology, Volume 1: Public Health and Hazardous Wastes

    Unknown

  • QT interval variability in body surface ECG: measurement, physiological basis, and clinical value: position statement and consensus guidance endorsed by the European Heart�…

    Unknown

  • A novel extended local-binary-pattern operator for texture analysis

    Hui Zhou;Runsheng Wang;Cheng Wang

  • Voltammetric studies of the oxygen-titanium binary system in molten calcium chloride

    Unknown

  • LO-Net: Deep Real-Time Lidar Odometry

    Qing Li;Shaoyang Chen;Cheng Wang;Xin Li

  • Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review

    Lingfei Ma;Ying Li;Jonathan Li;Cheng Wang

  • Road extraction in remote sensing data: A survey

    Unknown

  • Semiautomated Extraction of Street Light Poles From Mobile LiDAR Point-Clouds

    Yongtao Yu;Jonathan Li;Haiyan Guan;Cheng Wang

  • Automated Road Information Extraction From Mobile Laser Scanning Data

    Haiyan Guan;Jonathan Li;Yongtao Yu;Michael Chapman

  • Learning Hierarchical Features for Automated Extraction of Road Markings From 3-D Mobile LiDAR Point Clouds

    Yongtao Yu;Jonathan Li;Haiyan Guan;Fukai Jia

  • Toward better boundary preserved supervoxel segmentation for 3D point clouds

    Yangbin Lin;Cheng Wang;Dawei Zhai;Wei Li

  • NormalNet: A voxel-based CNN for 3D object classification and retrieval

    Cheng Wang;Ming Cheng;Ferdous Sohel;Mohammed Bennamoun

  • PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery

    Xian Sun;Peijin Wang;Cheng Wang;Yingfei Liu

  • A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds

    Chenglu Wen;Xiaotian Sun;Jonathan Li;Jonathan Li;Cheng Wang

  • 3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association

    Hai Wu;Wenkai Han;Chenglu Wen;Xin Li

  • Line segment extraction for large scale unorganized point clouds

    Yangbin Lin;Cheng Wang;Jun Cheng;Bili Chen

  • CasA: A Cascade Attention Network for 3-D Object Detection From LiDAR Point Clouds

    Unknown

  • Vehicle Detection in High-Resolution Aerial Images via Sparse Representation and Superpixels

    Ziyi Chen;Cheng Wang;Chenglu Wen;Xiuhua Teng

  • LiDAR-Video Driving Dataset: Learning Driving Policies Effectively

    Yiping Chen;Jingkang Wang;Jonathan Li;Cewu Lu

  • Review of research progress on the electrical properties and modification of mineral insulating oils used in power transformers

    Unknown

Frequent Co-Authors

Jonathan Li
Jonathan Li University of Waterloo
Chenglu Wen
Chenglu Wen Xiamen University
Yongtao Yu
Yongtao Yu Huaiyin Institute of Technology
See Leang Chin
See Leang Chin Université Laval
xin li
xin li Louisiana State University
Ying Wu
Ying Wu Northwestern University
Guangyuan He
Guangyuan He Huazhong University of Science and Technology
Guangxiao Yang
Guangxiao Yang Huazhong University of Science and Technology
Yulan Guo
Yulan Guo Sun Yat-sen University
Naser El-Sheimy
Naser El-Sheimy University of Calgary

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