Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning

Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning

We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds. Our model predicts rasterized occupancy maps for three vegetation strata corresponding to lower, medium, and higher cover. Our weakly-supervised training scheme allows o...

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Bibliographic Details
Main Authors: Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet, Nesrine Chehata
Format: Article
Language:English
Published: Elsevier 2022-08-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Vegetation analysis
Stratum
Airborne LiDAR
Deep neural networks
Weakly-supervised learning
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843222000656

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