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

2021 | journal article; research paper. A publication with affiliation to the University of Göttingen.

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​Seidel, Dominik, et al. "Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning​." ​Frontiers in Plant Science, vol. 12, ​2021, , ​doi: 10.3389/fpls.2021.635440. 

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Seidel, Dominik ; Annighöfer, Peter ; Thielman, Anton; Seifert, Quentin Edward ; Thauer, Jan-Henrik; Glatthorn, Jonas; Ehbrecht, Martin; Kneib, Thomas ; Ammer, Christian 
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 high. We could partly relate this to the successful application of the image augmentation technique, improving our result by 6% in total and 13, 14, and 24% for ash, oak and pine, respectively. The introduced approach is hence not only applicable to small-sized datasets, it is also computationally effective since it relies on 2D instead of 3D data to be processed in the CNN. Our approach was faster and more accurate when compared to the point cloud-based “PointNet” approach.
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Frontiers in Plant Science 
Fakultät für Forstwissenschaften und Waldökologie ; Burckhardt-Institut ; Abteilung Räumliche Strukturen und Digitalisierung von Wäldern ; Abteilung Waldbau und Waldökologie der gemäßigten Zonen 
Open-Access-Publikationsfonds 2021



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