Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning.

ABSTRACT: Automated species classification from 3D point clouds is still a challenge. It is, however, an important task for laser scanning-based forest inventory, ecosystem models, and to support forest management. Here, we tested the performance of an image classification approach based on convolutional neural networks (CNNs) with the aim to classify 3D point clouds of seven tree species based on 2D representation in a computationally efficient way. We were particularly interested in how the approach would perform with artificially increased training data size based on image augmentation techniques. Our approach yielded a high classification accuracy (86%) and the confusion matrix revealed that despite rather small sample sizes of the training data for some tree species, classification accuracy was

SUBMITTER: Seidel D 

PROVIDER: S-EPMC7902704 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature