108 citations · 312 across the 5 of their papers we have counts for
6 papers
Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees
Hamid Hamraz, Nathan B. Jacobs, Marco A. Contreras +1
The purpose of this study was to investigate the use of deep learning for coniferous/deciduous classification of individual trees from airborne LiDAR data. To enable efficient proc…
Remote sensing of forests using discrete return airborne LiDAR
Hamid Hamraz, Marco A. Contreras
Airborne discrete return light detection and ranging (LiDAR) point clouds covering forested areas can be processed to segment individual trees and retrieve their morphological attr…
Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds
Hamid Hamraz, Marco A. Contreras, Jun Zhang
Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmen…
A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data
Hamid Hamraz, Marco A. Contreras, Jun Zhang
This paper presents a non-parametric approach for segmenting trees from airborne LiDAR data in deciduous forests. Based on the LiDAR point cloud, the approach collects crown inform…
A scalable approach for tree segmentation within small-footprint airborne LiDAR data
Hamid Hamraz, Marco A. Contreras, Jun Zhang
This paper presents a distributed approach that scales up to segment tree crowns within a LiDAR point cloud representing an arbitrarily large forested area. The approach uses a sin…
Vertical stratification of forest canopy for segmentation of under-story trees within small-footprint airborne LiDAR point clouds
Hamid Hamraz, Marco A. Contreras, Jun Zhang
Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure even of understory layers can be derived. This paper presents a tree segmenta…