World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
7732
World Ranking
7990
National Ranking
65

Xinlian Liang 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 Xinlian Liang 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: 104 publications — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Xinlian Liang 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 Xinlian Liang 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: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Overview

Xinlian Liang is affiliated with the Finnish Geospatial Research Institute in Finland. Their research primarily focuses on environmental science, with a significant number of publications in related subfields such as environmental engineering, nature and landscape conservation, geology, aerospace engineering, and insect science.

The scientist's work extensively covers topics including remote sensing and LiDAR applications, forest ecology and management, 3D surveying and cultural heritage, forest ecology and biodiversity studies, remote sensing in agriculture, robotics and sensor-based localization, and urban heat island mitigation.

Xinlian Liang has published research in several prominent venues, reflecting a focus on geospatial and environmental themes. Frequent publication venues include:

  • The "International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • Forest Ecosystems
  • Remote Sensing of Environment
  • IEEE Transactions on Geoscience and Remote Sensing
  • SSRN Electronic Journal

The scientist has collaborated frequently with others in their field, including Yunsheng Wang, Juha Hyyppä, Antero Kukko, Hanwen Qi, and Martin Mokroš.

Several recent papers by Xinlian Liang characterize the focus on advanced remote sensing technologies and forest measurements. These include:

  • "Under-canopy UAV laser scanning for accurate forest field measurements" (2020, ISPRS Journal of Photogrammetry and Remote Sensing)
  • "Accurate derivation of stem curve and volume using backpack mobile laser scanning" (2020, ISPRS Journal of Photogrammetry and Remote Sensing)
  • "Is field-measured tree height as reliable as believed - Part II, A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest" (2020, ISPRS Journal of Photogrammetry and Remote Sensing)
  • "Novel low-cost mobile mapping systems for forest inventories as terrestrial laser scanning alternatives" (2021, International Journal of Applied Earth Observation and Geoinformation)
  • "Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions" (2022, IEEE Geoscience and Remote Sensing Magazine)

Liang's work predominantly employs UAV laser scanning, mobile laser scanning, and close-range remote sensing technologies to improve forest inventory methods and ecological measurements.

Best Publications

  • Terrestrial laser scanning in forest inventories

    Xinlian Liang;Xinlian Liang;Ville Kankare;Ville Kankare;Juha Hyyppä;Juha Hyyppä;Yunsheng Wang;Yunsheng Wang

  • ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

    Unknown

  • International benchmarking of terrestrial laser scanning approaches for forest inventories

    Xinlian Liang;Juha Hyyppä;Harri Kaartinen;Harri Kaartinen;Matti Lehtomäki

  • Is field-measured tree height as reliable as believed – A comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forest

    Yunsheng Wang;Yunsheng Wang;Matti Lehtomäki;Matti Lehtomäki;Xinlian Liang;Xinlian Liang;Jiri Pyörälä;Jiri Pyörälä;Jiri Pyörälä

  • Automatic Stem Mapping Using Single-Scan Terrestrial Laser Scanning

    Xinlian Liang;P. Litkey;J. Hyyppa;H. Kaartinen

  • Automated Stem Curve Measurement Using Terrestrial Laser Scanning

    Xinlian Liang;Ville Kankare;Xiaowei Yu;Juha Hyyppa

  • International Benchmarking of the Individual Tree Detection Methods for Modeling 3-D Canopy Structure for Silviculture and Forest Ecology Using Airborne Laser Scanning

    Yunsheng Wang;Juha Hyyppa;Xinlian Liang;Harri Kaartinen

  • Automatic stem mapping by merging several terrestrial laser scans at the feature and decision levels.

    Xinlian Liang;Juha Hyyppä

  • Novel low-cost mobile mapping systems for forest inventories as terrestrial laser scanning alternatives

    Unknown

  • Under-canopy UAV laser scanning for accurate forest field measurements

    Eric Hyyppä;Juha Hyyppä;Juha Hyyppä;Teemu Hakala;Antero Kukko;Antero Kukko

  • Accurate derivation of stem curve and volume using backpack mobile laser scanning

    Eric Hyyppä;Antero Kukko;Antero Kukko;Risto Kaijaluoto;Joanne C. White

  • The Use of a Mobile Laser Scanning System for Mapping Large Forest Plots

    Xinlian Liang;Juha Hyyppa;Antero Kukko;Harri Kaartinen

  • Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements

    Xinlian Liang;Yunsheng Wang;Jiri Pyörälä;Matti Lehtomäki

  • Tree mapping using airborne, terrestrial and mobile laser scanning – A case study in a heterogeneous urban forest

    Markus Holopainen;Ville Kankare;Mikko Vastaranta;Xinlian Liang

  • Feasibility of Terrestrial laser scanning for collecting stem volume information from single trees

    Ninni Saarinen;Ninni Saarinen;Ville Kankare;Ville Kankare;Mikko Vastaranta;Mikko Vastaranta;Ville Luoma;Ville Luoma

  • Accuracy of Kinematic Positioning Using Global Satellite Navigation Systems under Forest Canopies

    Harri Kaartinen;Juha Hyyppä;Mikko Vastaranta;Antero Kukko

  • Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

    Xiaowei Yu;Juha Hyyppä;Mika Karjalainen;Kimmo Nurminen

  • Possibilities of a Personal Laser Scanning System for Forest Mapping and Ecosystem Services

    Xinlian Liang;Antero Kukko;Harri Kaartinen;Juha Hyyppä

  • An Integrated GNSS/INS/LiDAR-SLAM Positioning Method for Highly Accurate Forest Stem Mapping

    Chuang Qian;Hui Liu;Jian Tang;Yuwei Chen

  • Forest Data Collection Using Terrestrial Image-Based Point Clouds From a Handheld Camera Compared to Terrestrial and Personal Laser Scanning

    Xinlian Liang;Yunsheng Wang;Anttoni Jaakkola;Antero Kukko

  • The Use of a Hand-Held Camera for Individual Tree 3D Mapping in Forest Sample Plots

    Xinlian Liang;Anttoni Jaakkola;Yunsheng Wang;Juha Hyyppä

  • In-situ measurements from mobile platforms: An emerging approach to address the old challenges associated with forest inventories

    Xinlian Liang;Xinlian Liang;Antero Kukko;Antero Kukko;Antero Kukko;Juha Hyyppä;Juha Hyyppä;Matti Lehtomäki;Matti Lehtomäki

Frequent Co-Authors

Juha Hyyppä
Juha Hyyppä Finnish Geospatial Research Institute
Antero Kukko
Antero Kukko Aalto University
Harri Kaartinen
Harri Kaartinen University of Turku
Markus Holopainen
Markus Holopainen University of Helsinki
Mikko Vastaranta
Mikko Vastaranta University of Eastern Finland
Xiaowei Yu
Xiaowei Yu Finnish Geospatial Research Institute
Anttoni Jaakkola
Anttoni Jaakkola Finnish Geospatial Research Institute
Hannu Hyyppä
Hannu Hyyppä Aalto University
Ruizhi Chen
Ruizhi Chen Wuhan University
Eija Honkavaara
Eija Honkavaara Finnish Geospatial Research Institute

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