Technical Program: Paper Detail || IEEE IGARSS 2023 || Pasadena, California, USA || 16

WE2.R3.3

TREE SPECIES CLASSIFICATION USING DEEP LEARNING BASED 3D POINT CLOUD TRANSFORMER ON AIRBORNE LIDAR DATA

Lanying Wang, Dening Lu, Weikai Tan, University of Waterloo, Canada; Haiyan Guan, Nanjing University of Information Science and Technology, China; Yiping Chen, Sun Yat-sen University, China; Jonathan Li, University of Waterloo, Canada

Session:

WE2.R3: LiDAR Point Clouds Intelligent Processing Oral

Location:

Room 107

Presentation Time:

Wed, 19 Jul, 10:39 - 10:51 Pacific Time (UTC -8)

Session Co-Chairs:

Yiping Chen, Sun Yat-sen University and Jonathan Li, University of Waterloo

Session Manager:

HUMAIRA SANAM

Session WE2.R3

WE2.R3.1: A DEEP ACTIVE LEARNING FRAMEWORK FOR SEMANTIC LABELING OF POINT CLOUDS

Hongjing Wu, Huan Luo, Wenzhong Guo, Fuzhou University, China

WE2.R3.2: GRAPH NEURAL NETWORK WITH MULTI-KERNEL LEARNING FOR MULTISPECTRAL POINT CLOUD CLASSIFICATION

Zifeng Zhang, Qingwang Wang, Mingye Wang, Tao Shen, Kunming University of Science and Technology, China

WE2.R3.3: TREE SPECIES CLASSIFICATION USING DEEP LEARNING BASED 3D POINT CLOUD TRANSFORMER ON AIRBORNE LIDAR DATA

WE2.R3.4: INVESTIGATING THE IMPACT OF POINT CLOUD DENSITY ON SEMANTIC SEGMENTATION PERFORMANCE USING VIRTUAL LIDAR IN BOREAL FOREST

Olivier Stocker, Reza Mahmoudi Kouhi, Eric Guilbert, Laval University, Canada; Antonio Ferraz, NASA, Canada; Thierry Badard, Laval University, Canada

WE2.R3.5: Optimal Transport for Change Detection on LiDAR Point Clouds

Marco Fiorucci, Center for Cultural Heritage Technology, Istituto Italiano di Tecnologia, Kyoto University, Italy; Peter Naylor, RIKEN Center for Advanced Intelligence Project (AIP), Japan; Makoto Yamada, Kyoto University, RIKEN Center for Advanced Intelligence Project (AIP), Okinawa Institute of Science and Technology, Japan