Search Results for author: Marco A. Contreras

Found 5 papers, 0 papers with code

Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees

no code implementations24 Feb 2018 Hamid Hamraz, Nathan B. Jacobs, Marco A. Contreras, Chase H. Clark

Lastly, the classification accuracies of overstory trees (~90%) were more balanced than those of understory trees (~90% deciduous and ~65% coniferous), which is likely due to the incomplete capture of understory tree crowns via airborne LiDAR.

Classification Data Augmentation +1

Remote sensing of forests using discrete return airborne LiDAR

no code implementations17 Jul 2017 Hamid Hamraz, Marco A. Contreras

In this chapter, we present (i) a robust segmentation method that avoids a priori assumptions about the canopy structure, (ii) a vertical canopy stratification procedure that improves segmentation of understory trees, (iii) an occlusion model for estimating the point density of each canopy stratum, and (iv) a distributed computing approach for efficient processing at the forest level.

Distributed Computing

Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds

no code implementations17 Feb 2017 Hamid Hamraz, Marco A. Contreras, Jun Zhang

Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers.

A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data

no code implementations1 Jan 2017 Hamid Hamraz, Marco A. Contreras, Jun Zhang

This paper presents a non-parametric approach for segmenting trees from airborne LiDAR data in deciduous forests.

Vertical stratification of forest canopy for segmentation of under-story trees within small-footprint airborne LiDAR point clouds

no code implementations31 Dec 2016 Hamid Hamraz, Marco A. Contreras, Jun Zhang

This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method.

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