Xiaowei Yu

Environmental Engineering · 2008–2020

A researcher in the Science corpus: 12 papers; trusts 12 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

Whom Xiaowei Yu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Tomas Brandtberg
  2. J. Reitberger
  3. J. Repola
  4. Erik Næsset
  5. J. Kalliovirta
  6. Cyril Goutte
  7. Juha Hyyppä
  8. Johan Holmgren
  9. Mikko Vastaranta
  10. Joanne C. White

Who trusts Xiaowei Yu overall

  1. Hooman Latifi
  2. Wei Yao
  3. M. Wulder
  4. M. Sumnall
  5. Feng A. Zhao
  6. Joanne C. White
  7. L. Ruiz
  8. Fabian Ewald Fassnacht
  9. Juha Hyyppä
  10. Barbara Koch

Papers from Xiaowei Yu's perspective

Ordered by how strongly the researchers Xiaowei Yu trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Automatic forest inventory parameter determination from terrestrial laser scanner data
  2. Stem biomass estimation based on stem reconstruction from terrestrial laser scanning point clouds
  3. Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods
  4. Three-dimensional reconstruction of stems for assessment of taper, sweep and lean based on laser scanning of standing trees
  5. Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery
  6. The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning
  7. An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning (Remote Sensing)
  8. A progressive morphological filter for removing nonground measurements from airborne LIDAR data (IEEE Transactions on Geoscience and Remote Sensing)
  9. A Practical Guide to Splines
  10. Automatic Stem Mapping Using Single-Scan Terrestrial Laser Scanning (IEEE Transactions on Geoscience and Remote Sensing)

Their papers