D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 3,758 69 World Ranking 8814 National Ranking 79

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Computer vision

Xinlian Liang mainly investigates Remote sensing, Point cloud, Tree, Forest inventory and Laser scanning. His studies in Remote sensing integrate themes in fields like Data collection, Mean squared error, Vegetation, Mobile laser scanning and Calibration. His study looks at the relationship between Point cloud and fields such as Lidar, as well as how they intersect with chemical problems.

His Tree research is multidisciplinary, incorporating elements of Crown and Plot. His Forest inventory research includes elements of Sample, Field, Digital elevation model and Scale. His Laser scanning study frequently involves adjacent topics like Diameter at breast height.

His most cited work include:

  • Terrestrial laser scanning in forest inventories (276 citations)
  • Terrestrial laser scanning in forest inventories (276 citations)
  • Automatic Stem Mapping Using Single-Scan Terrestrial Laser Scanning (190 citations)

What are the main themes of his work throughout his whole career to date?

Xinlian Liang mostly deals with Remote sensing, Point cloud, Tree, Laser scanning and Forest inventory. His Lidar study in the realm of Remote sensing interacts with subjects such as Reference data. His work deals with themes such as Photogrammetry and Diameter at breast height, which intersect with Point cloud.

His Tree study combines topics in areas such as Field, Plot, Ground truth, Crown and Taiga. As part of one scientific family, Xinlian Liang deals mainly with the area of Laser scanning, narrowing it down to issues related to the Field, and often Scale and Sample. The concepts of his Forest inventory study are interwoven with issues in GNSS applications and Basal area.

He most often published in these fields:

  • Remote sensing (72.73%)
  • Point cloud (61.62%)
  • Tree (49.49%)

What were the highlights of his more recent work (between 2019-2021)?

  • Remote sensing (72.73%)
  • Point cloud (61.62%)
  • Forest inventory (35.35%)

In recent papers he was focusing on the following fields of study:

Remote sensing, Point cloud, Forest inventory, Laser scanning and Tree are his primary areas of study. The Remote sensing study combines topics in areas such as Terrestrial laser scanning and Canopy. His Point cloud research incorporates themes from Light detection, Convolutional neural network and Database.

His Forest inventory research incorporates elements of Environmental resource management and Benchmark. The various areas that Xinlian Liang examines in his Laser scanning study include Lidar, Real tree and Deciduous. His Tree research integrates issues from Mean squared error and Plot.

Between 2019 and 2021, his most popular works were:

  • Accurate derivation of stem curve and volume using backpack mobile laser scanning (15 citations)
  • Under-canopy UAV laser scanning for accurate forest field measurements (14 citations)
  • CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description. (12 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Computer vision

Xinlian Liang focuses on Laser scanning, Remote sensing, Lidar, Point cloud and Reference data. His Laser scanning study frequently draws parallels with other fields, such as Forest inventory. He has researched Forest inventory in several fields, including Real tree and Deciduous.

Xinlian Liang has included themes like Matching and Deep learning, Autoencoder in his Lidar study. He interconnects Photogrammetry, Data processing and Plot in the investigation of issues within Point cloud. His work in Reference data incorporates the disciplines of Tree, Blossom algorithm, Volume, Scanner and Mean squared error.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

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 Journal of Photogrammetry and Remote Sensing (2016)

380 Citations

Automatic Stem Mapping Using Single-Scan Terrestrial Laser Scanning

Xinlian Liang;P. Litkey;J. Hyyppa;H. Kaartinen.
IEEE Transactions on Geoscience and Remote Sensing (2012)

232 Citations

Automated Stem Curve Measurement Using Terrestrial Laser Scanning

Xinlian Liang;Ville Kankare;Xiaowei Yu;Juha Hyyppa.
IEEE Transactions on Geoscience and Remote Sensing (2014)

170 Citations

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

Xinlian Liang;Juha Hyyppä.
Sensors (2013)

139 Citations

International benchmarking of terrestrial laser scanning approaches for forest inventories

Xinlian Liang;Juha Hyyppä;Harri Kaartinen;Harri Kaartinen;Matti Lehtomäki.
Isprs Journal of Photogrammetry and Remote Sensing (2018)

137 Citations

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.
Urban Forestry & Urban Greening (2013)

106 Citations

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.
Isprs Journal of Photogrammetry and Remote Sensing (2019)

103 Citations

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.
Remote Sensing (2015)

103 Citations

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

Xinlian Liang;Juha Hyyppa;Antero Kukko;Harri Kaartinen.
IEEE Geoscience and Remote Sensing Letters (2014)

94 Citations

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.
IEEE Transactions on Geoscience and Remote Sensing (2016)

90 Citations

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Best Scientists Citing Xinlian Liang

Juha Hyyppä

Juha Hyyppä

Finnish Geospatial Research Institute

Publications: 75

Mikko Vastaranta

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University of Eastern Finland

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Markus Holopainen

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Harri Kaartinen

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Hannu Hyyppä

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Antero Kukko

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Kim Calders

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Qinghua Guo

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Nicholas C. Coops

Nicholas C. Coops

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Petteri Alho

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Anttoni Jaakkola

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Mathias Disney

Mathias Disney

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Norbert Pfeifer

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Eija Honkavaara

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Shaun R. Levick

Shaun R. Levick

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Joanne C. White

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