TH3.R18.1
ATTENTION-ENHANCED DEEP LEARNING FOR DBH ESTIMATION FROM TREE LIDAR POINT CLOUDS
Hang Zhao, University of Kansas, United States; Jingjing Qiang, Royal Melbourne Institute of Technology, Australia
Session:
TH3.R18: Remote sensing for generating robust datasets for forest health assessment and management planning (3/3) Oral
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Thu, 13 Aug, 13:45 - 14:00
Presentation
Discussion
Resources
No resources available.
Session TH3.R18
TH3.R18.1: ATTENTION-ENHANCED DEEP LEARNING FOR DBH ESTIMATION FROM TREE LIDAR POINT CLOUDS
Hang Zhao, University of Kansas, United States; Jingjing Qiang, Royal Melbourne Institute of Technology, Australia
TH3.R18.2: COMPARING ABOVEGROUND BIOMASS (AGB) ESTIMATION MODELS USING MULTI-SOURCE LIDAR AND SENTINEL-2 DATA
Chenye Zhang, Parinaz Rahimzadeh-Bajgiran, Daniel Hayes, University of Maine, United States
TH3.R18.3: MANGO: A Global Single-Date Paired Dataset for Mangrove Segmentation
Junhyuk Heo, Beomkyu Choi, Hyunjin Shin, Darongsae Kwon, TelePIX, Korea (South)
TH3.R18.5: An Open-Source Workflow for Building Voxelized Models of Forests with Airborne Lidar and Hyperspectral Data
Kyla Dahlin, Meicheng Shen, Alanna Post, T. Renae Gizzo, Anthony Bowman, Michigan State University, United States; Aaron Kamoske, USDA Forest Service, United States; Zachary Butterfield, Michigan State University, United States; Adriana Uscanga, University of Minnesota, United States; Courtney Ross, Sandhya Sharma, Madelyn Immoos, Scott Stark, Michigan State University, United States; Shawn Serbin, NASA Goddard Space Flight Center, United States
Contacts