Abstract
Abstract. Street trees are common features and important assets in urban scenes. They are huge in numbers and are constantly changing, thus are difficult to monitor on a regular basis. A method of automatic extraction and dynamic analysis of street trees based on mobile LiDAR data is proposed. First, ground and low objects are filtered from the point clouds. Then, based on a geometric tree model and semantic information, each tree point cloud is extracted, and geometrical parameters such as location, trunk diameter, trunk structure line, tree height, crown width, and crown volume of each tree is obtained. A dynamic analysis combined with the growing characteristics of trees is conducted to compare and analyse the street trees from different epochs, in order to understand whether the trees have grown or been pruned, replanted, or displaced. The proposed algorithm was tested on three epochs of mobile LiDAR data, obtained in 2010, 2016 and 2018, respectively. Experimental results showed that the proposed method was able to accurately detect trees and extract tree parameters for detailed dynamics analysis.
Highlights
Most cities have a large number of street trees, which play an important role in urban ecosystems
Point cloud, this paper proposes a method of automatic extraction of street trees by combining geometric parameters and semantic information, and a dynamic analysis framework for detailed change analysis
The contributions of this paper are: (1) a street tree parameter model is constructed according to the characteristics of trees in the mobile LiDAR point cloud; (2) based on the tree parameter model, a detailed parameter extraction algorithm for the street tree is proposed; (3) based on multi-temporal data, an analysis framework for street tree dynamics understanding is provided
Summary
Most cities have a large number of street trees, which play an important role in urban ecosystems. They have to be regularly monitored to understand their dynamics and to maintain their functions. Mobile LiDAR systems have obvious advantages over other data acquisition techniques as they can collect accurate and detailed 3D point cloud of trees on both sides of the streets in a whole city, covering relatively complete trunk and crown information from a side-view. Using mobile LiDAR point cloud, this paper proposes a method of automatic extraction of street trees by combining geometric parameters and semantic information, and a dynamic analysis framework for detailed change analysis. The contributions of this paper are: (1) a street tree parameter model is constructed according to the characteristics of trees in the mobile LiDAR point cloud; (2) based on the tree parameter model, a detailed parameter extraction algorithm for the street tree is proposed; (3) based on multi-temporal data, an analysis framework for street tree dynamics understanding is provided
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