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 44 Citations 7,073 116 World Ranking 3682 National Ranking 24
Environmental Sciences D-index 44 Citations 7,033 138 World Ranking 2689 National Ranking 40

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Optics

Harri Kaartinen mainly investigates Remote sensing, Laser scanning, Point cloud, Tree and Forest inventory. His Remote sensing research integrates issues from Mobile laser scanning, Canopy, Calibration and Vegetation. As part of the same scientific family, Harri Kaartinen usually focuses on Laser scanning, concentrating on Feature extraction and intersecting with Volume estimation.

His Point cloud research is multidisciplinary, incorporating elements of Photogrammetry and Lidar. Harri Kaartinen interconnects Spatial analysis and Plot in the investigation of issues within Tree. His research in Plot intersects with topics in Terrestrial laser scanning, Field and Ground truth.

His most cited work include:

  • Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data (327 citations)
  • An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning (302 citations)
  • Terrestrial laser scanning in forest inventories (276 citations)

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

Remote sensing, Laser scanning, Point cloud, Tree and Lidar are his primary areas of study. Many of his research projects under Remote sensing are closely connected to Reference data with Reference data, tying the diverse disciplines of science together. His Laser scanning research incorporates themes from Change detection, Computer vision, Artificial intelligence and Mobile mapping.

His work in Point cloud covers topics such as Terrain which are related to areas like Elevation. His work deals with themes such as Mean squared error, Statistics, Volume, Plot and Ground truth, which intersect with Tree. His Lidar research is multidisciplinary, relying on both Ranging, Data processing, Water content and Standard deviation.

He most often published in these fields:

  • Remote sensing (67.58%)
  • Laser scanning (53.88%)
  • Point cloud (37.90%)

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

  • Remote sensing (67.58%)
  • Laser scanning (53.88%)
  • Point cloud (37.90%)

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

His primary scientific interests are in Remote sensing, Laser scanning, Point cloud, Lidar and Atmospheric sciences. His work in Laser scanning addresses issues such as Canopy, which are connected to fields such as Taiga and Volume. His Point cloud study combines topics in areas such as Quality, Photogrammetry, Inertial measurement unit and Database.

His Lidar research incorporates elements of Data processing and Photon. The various areas that Harri Kaartinen examines in his Atmospheric sciences study include Terrestrial laser scanning, Intensity and Temporal scales. His Tree research includes elements of Mean squared error, Forest inventory, Scanner 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)
  • 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)

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

  • Artificial intelligence
  • Statistics
  • Optics

Harri Kaartinen focuses on Laser scanning, Reference data, Remote sensing, Tree and Lidar. His Laser scanning study integrates concerns from other disciplines, such as Field, Canopy, Backpack and Taiga. His research integrates issues of Mean squared error, Forest inventory, Scanner, Volume and Blossom algorithm in his study of Tree.

His work carried out in the field of Lidar brings together such families of science as Point cloud, Data processing and Plot.

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

An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning

Harri Kaartinen;Juha Hyyppä;Xiaowei Yu;Mikko Vastaranta.
Remote Sensing (2012)

430 Citations

Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data

Pasi Raumonen;Mikko Kaasalainen;Markku Åkerblom;Sanna Kaasalainen.
Remote Sensing (2013)

421 Citations

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 detection of harvested trees and determination of forest growth using airborne laser scanning

Xiaowei Yu;Juha Hyyppä;Harri Kaartinen;Matti Maltamo.
Remote Sensing of Environment (2004)

360 Citations

A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements

Anttoni Jaakkola;Juha Hyyppä;Antero Kukko;Xiaowei Yu.
Isprs Journal of Photogrammetry and Remote Sensing (2010)

313 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

Detection of Vertical Pole-Like Objects in a Road Environment Using Vehicle-Based Laser Scanning Data

Matti Lehtomäki;Anttoni Jaakkola;Juha Hyyppä;Antero Kukko.
Remote Sensing (2010)

229 Citations

Multiplatform Mobile Laser Scanning: Usability and Performance

Antero Kukko;Harri Kaartinen;Juha Hyyppä;Yuwei Chen.
Sensors (2012)

182 Citations

Seamless Mapping of River Channels at High Resolution Using Mobile LiDAR and UAV-Photography

Claude Flener;Matti Vaaja;Anttoni Jaakkola;Anssi Krooks.
Remote Sensing (2013)

176 Citations

Advances in Forest Inventory Using Airborne Laser Scanning

Juha Hyyppä;Xiaowei Yu;Hannu Hyyppä;Mikko Vastaranta.
Remote Sensing (2012)

162 Citations

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