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

Dominik Seidel¹, Peter Annighöfer², Anton Thielman³

¹Faculty of Forest Sciences, Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Göttingen, Germany.

Frontiers in Plant Science

|March 1, 2021

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This summary is machine-generated.

This study introduces an efficient image classification method using convolutional neural networks (CNNs) for automated tree species identification from 3D point clouds. The approach significantly improves accuracy, especially with augmented data, outperforming 3D-based methods.

Area of Science:

Forestry and ecological informatics
Computer vision and machine learning
Remote sensing applications

Background:

Automated species classification from 3D point clouds is crucial for forest inventory and management.
Existing methods face challenges in efficiency and accuracy with complex 3D data.

Purpose of the Study:

To evaluate an image classification approach using convolutional neural networks (CNNs) for classifying tree species from 3D point clouds.
To assess the impact of data augmentation techniques on classification accuracy.
To compare the performance against 3D point cloud-based methods.

Main Methods:

Utilized a 2D image representation of 3D point clouds for classification with CNNs.
Applied image augmentation techniques to artificially increase training data size.

Keywords:

artificial intelligence convolutional neural networks laser scanning machine-learning tree species classification

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Compared the developed approach with the 3D point cloud-based "PointNet" method.

Main Results:

Achieved a high overall classification accuracy of 86%.
Image augmentation improved results by 6% overall, with specific gains for ash (13%), oak (14%), and pine (24%).
The 2D CNN approach demonstrated higher speed and accuracy compared to PointNet.

Conclusions:

The 2D image-based CNN approach is effective and computationally efficient for automated tree species classification from 3D point clouds.
Data augmentation is a valuable technique for improving classification performance, particularly with limited training data.
This method offers a promising alternative for large-scale forest inventory and management applications.