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PointCNN-Based Individual Tree Detection Using LiDAR Point Clouds

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Advances in Computer Graphics (CGI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13002))

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Abstract

Due to the rapid development of deep learning technology in recent years, many scholars have applied deep learning technology to the field of remote sensing imagery. But few have directly applied LiDAR point clouds to 3D neural networks for tree detection. And the existing methods usually have better detection results in a specific single scene, but in some complex scenes, such as containing diverse types of trees, urban forests and high forest density, the detection results are not satisfactory. Therefore, this paper presents a PointCNN-based method of 3D tree detection using LiDAR point clouds, which aims to improve the detection accuracy of trees in complex scenes and versatility. This method first builds a canopy height model (CHM) using raw LiDAR point clouds and obtains rough seed points on CHM. Then it extracts the detection samples consisting of single tree's point cloud data based on the rough seed points. Next, the 3D-CNN classifier based on PointCNN is adopted to classify detection samples, and the classification results are used for filtering seed points. Finally, our method performs the tree stagger analysis on those close seed points. This study selected twelve experimental plots from study areas in Bend, Central Oregon, USA. Based on the results of our experiments, the highest matching score and average score reached 91.0 and 88.3. Experimental results show that our method can effectively extract tree information in complex scenes.

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Acknowledgement

This research was supported by the National Natural Science Foundation of China under Grant No. 62072405 and Zhejiang Provincial Natural Science Foundation of China under Grant No. LGF20F020017.

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Authors and Affiliations

  1. Zhejiang University of Technology, Hangzhou, China

    Wenyuan Ying, Tianyang Dong, Zhanfeng Ding & Xinpeng Zhang

Authors
  1. Wenyuan Ying
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  2. Tianyang Dong
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  3. Zhanfeng Ding
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  4. Xinpeng Zhang
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Corresponding author

Correspondence to Tianyang Dong .

Editor information

Editors and Affiliations

  1. University of Geneva, Carouge, Switzerland

    Nadia Magnenat-Thalmann

  2. University of Minnesota, Minneapolis, MN, USA

    Victoria Interrante

  3. EPFL, Lausanne, Switzerland

    Daniel Thalmann

  4. University of Crete, Heraklion, Crete, Greece

    George Papagiannakis

  5. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  6. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  7. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

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Ying, W., Dong, T., Ding, Z., Zhang, X. (2021). PointCNN-Based Individual Tree Detection Using LiDAR Point Clouds. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2021. Lecture Notes in Computer Science(), vol 13002. Springer, Cham. https://doi.org/10.1007/978-3-030-89029-2_7

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  • DOI: https://doi.org/10.1007/978-3-030-89029-2_7

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  • Online ISBN: 978-3-030-89029-2

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