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Shape classification guided method for automated extraction of urban trees from terrestrial laser scanning point clouds

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Abstract

Accurate detection and extraction of individual trees is one of hottest topics, which can be widely used in vehicles navigation, tree modeling, tree growth monitoring and urban green quantity estimation. The difficulty associated with individual trees extraction is the occlusion with other objects in cluttered point clouds of urban scenes, which inhibits the automatic extraction of individual trees. In this paper, we present a comprehensive framework that can be used to extract individual trees from terrestrial scanned outdoor scene. In our framework, a bottom-up method by shape-guided classification is achieved to select the candidate tree crowns and tree trunks, and a novel three-stage shape merging rule containing localization, filtering, and matching (LFM) are proposed to generate a complete individual tree. The primary advantage of the proposed method is that it is independent of the quality of data and different shapes. We made comparison experiments of classification methods of support vector machine and random forest on the accuracy assessment. The effectiveness of the proposed framework was tested in five street scenarios in point clouds from Oakland outdoor MLS dataset. The results for the five test sites achieved tree detection rates higher than 97%; the overall accuracy was approximately 98%, and the completion quality of both procedures was 96%. Non-detected trees are always sparse which come from occlusions in the point cloud data; most misclassifications occurred in man-made pillars adjacent to trees and have the same height with tree trunk. Comparison experiments to the existing methods are made to illustrate the effectiveness of our method.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Nos.61871320, 61872291); in part by National Key Research and Development Project of China (2018YFB1004905); in part by China Postdoctoral Science Foundation (2014M552469); in part by Key laboratory project of Shaanxi Provincial Education Department (17JS099); in part by Shaanxi Postdoctoral Science Foundation (434015014).

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  1. Institute of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China

    Xiaojuan Ning, Ge Tian & Yinghui Wang

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  1. Xiaojuan Ning
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Correspondence to Xiaojuan Ning.

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Ning, X., Tian, G. & Wang, Y. Shape classification guided method for automated extraction of urban trees from terrestrial laser scanning point clouds. Multimed Tools Appl 80, 33357–33375 (2021). https://doi.org/10.1007/s11042-021-11328-7

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