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Publication . Article . 2020

See the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning

Zhouxin Xi; Chris Hopkinson; Stewart B. Rood; Derek R. Peddle;
Closed Access
Published: 01 Oct 2020 Journal: ISPRS Journal of Photogrammetry and Remote Sensing, volume 168, pages 1-16 (issn: 0924-2716, Copyright policy )
Publisher: Elsevier BV
Abstract

Abstract Determining tree species composition in natural forests is essential for effective forest management. Species classification at the individual tree level requires fine-scale traits which can be derived through terrestrial laser scanning (TLS) point clouds. A generalizable species classification framework also needs to decouple seasonal foliage variation from deciduous species, for which wood filtering is applicable. Different machine learning and deep learning models are feasible for wood filtering and species classification. We investigated 13 machine learning and deep learning classifiers for 9 species, and 15 classifiers for filtering wood points from TLS plot scans. Each classifier was evaluated using the criteria of mean Intersection over Union accuracy (mIoU), training stability and time cost. On average, deep learning classifiers outperformed machine learning classifiers by 10% and 5% in terms of wood and species classification mIoU, respectively. PointNet++ provided the best species classifier, with the highest mIoU (0.906), stability, and moderate time cost. Among wood classifiers, UNet achieved the top mIoU (0.839) while ResNet-50 was recommended for rapid trial and error testing. Across the classifications, the factors of input resolution, attributes and features were also analyzed. Hot zones of species classification with PointNet++ were visualized to indicate how AI interpret species traits.

Subjects by Vocabulary

Microsoft Academic Graph classification: Artificial intelligence business.industry business Pattern recognition Terrestrial laser scanning Trial and error Forest management Deep learning Point cloud Mathematics Classifier (UML) Tree species Lidar

Subjects

Computers in Earth Sciences, Computer Science Applications, Engineering (miscellaneous), Atomic and Molecular Physics, and Optics

Related Organizations
Funded by
NSERC
Project
  • Funder: Natural Sciences and Engineering Research Council of Canada (NSERC)
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