TH4.R18.4
A COMPARISON OF DEEP LEARNING OBJECT DETECTION ARCHITECTURES FOR CHM-BASED INDIVIDUAL TREE DETECTION IN DENSE CONIFEROUS FORESTS
Parsa Keshavarz Alamdari, California State University, Northridge, United States
Session:
TH4.R18: Biomass and Carbon Cycle I Oral
Track:
Land Applications
Location:
Monroe
Presentation Time:
Thursday, 13 August, 17:00 - 17:15
Presentation
Discussion
Resources
No resources available.
Session TH4.R18
TH4.R18.1: UAV Remote Sensing for Tree Analysis and Biomass Allocation in Northern Boreal Forests
Jacob Schladebach, Birgit Heim, Stefan Kruse, Alfred-Wegener Institute, Germany
TH4.R18.2: ASSESSMENT OF VEGETATION WATER DYNAMICS BY COMPARING MICROWAVE REMOTE SENSING SIGNALS FROM SATELLITES AND FIELD-BASED GNSS REFLECTOMETRY
Andrew Feldman, NASA Goddard Space Flight Center/University of Maryland, United States; William Smith, University of Arizona, United States; Alexandra Konings, Stanford University, United States; Shawn Serbin, NASA Goddard Space Flight Center, United States
TH4.R18.3: MAPPING FOLIAR LEAF TRAITS WITH PACE
Karl Huemmrich, University of Maryland Baltimore County, United States; Skye Caplan, Science Systems and Applications Inc., United States; Petya Campbell, University of Maryland Baltimore County, United States; Andres Baresch Aristizabal, University of Maryland College Park, United States; Loren Albert, Oregon State University, United States; Shawn Serbin, National Aeronautics and Space Administration Goddard Space Flight Center, United States
TH4.R18.4: A COMPARISON OF DEEP LEARNING OBJECT DETECTION ARCHITECTURES FOR CHM-BASED INDIVIDUAL TREE DETECTION IN DENSE CONIFEROUS FORESTS
Parsa Keshavarz Alamdari, California State University, Northridge, United States
Resources
No resources available.