2 citations · 3 across the 3 of their papers we have counts for
5 papers
Models to support forest inventory and small area estimation using sparsely sampled LiDAR: A case study involving G-LiHT LiDAR in Tanana, Alaska
Andrew O. Finley, Hans-Erik Andersen, Chad Babcock +3
A two-stage hierarchical Bayesian model is developed and implemented to estimate forest biomass density and total given sparsely sampled LiDAR and georeferenced forest inventory pl…
Artificial Perceptual Learning: Image Categorization with Weak Supervision
Chengliang Tang, María Uriarte, Helen Jin +2
Machine learning has achieved much success on supervised learning tasks with large sets of well-annotated training samples. However, in many practical situations, such strong and h…
Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping
Daniel Taylor-Rodriguez, Andrew O. Finley, Abhirup Datta +5
Gathering information about forest variables is an expensive and arduous activity. As such, directly collecting the data required to produce high-resolution maps over large spatial…
Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations
Chad Babcock, Andrew O. Finley, Hans-Erik Andersen +9
The goal of this research was to develop and examine the performance of a geostatistical coregionalization modeling approach for combining field inventory measurements, strip sampl…
Efficient algorithms for Bayesian Nearest Neighbor Gaussian Processes
Andrew O. Finley, Abhirup Datta, Bruce C. Cook +3
We consider alternate formulations of recently proposed hierarchical Nearest Neighbor Gaussian Process (NNGP) models (Datta et al., 2016a) for improved convergence, faster computin…