An automated approach for street trees detection using mobile laser scanner data

Husain, Arshad; Vaishya, Rakesh Chandra

doi:10.1016/j.rsase.2020.100371

Cited by 6 publications

(6 citation statements)

References 27 publications

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“…Most studies [24]- [26] have used the spatial elevation 69 section method to obtain the main stem or part of the branch 70 point cloud of trees for use as the basic information for tree 71 localization. However, if a uniform height threshold is used in 72 a scene with hilly terrain, point cloud filtering tends to miss 73 numerous tree trunks, which leads to failed localization.…”

Section: B Terrain Filtering and Trunk Filtering 68mentioning

confidence: 99%

An Individual Tree Segmentation Method From Mobile Mapping Point Clouds Based on Improved 3-D Morphological Analysis

Wang

Fan

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Street tree extraction based on the 3D mobile 1 mapping point cloud plays an important role in building smart 2 cities and creating highly accurate urban street maps. Existing 3 methods are often over-or under-segmented when segmenting 4 overlapping street tree canopies and extracting geometrically 5 complex trees. To address this problem, we propose a method 6 based on improved 3D morphological analysis for extracting 7 street trees from mobile laser scanner (MLS) point clouds. First, 8 the 3D semantic point cloud segmentation framework based 9 on Deep Learning is used for pre-classification of the original 10 point cloud to obtain the vegetation point cloud in the scene. 11 Considering the influence of terrain unevenness, the vegetation 12 point cloud is de-terraformed and slice point cloud containing 13 tree trunks is obtained through spatial filtering on height. On 14 this basis, a voxel-based region growing method constrained with 15 the changing rate of convex area is used to locate the stree trees. 16 Then we propose an progressive tree crown segmentation method, 17 which first completed the preliminary individual segmentation 18 of the tree crown point cloud based on the voxel-based region 19 growth constrained by the minimum increment rule, and then 20 optimizes the crown edges by "valley" structure-based clustering.

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Section: B Terrain Filtering and Trunk Filtering 68mentioning

confidence: 99%

An Individual Tree Segmentation Method From Mobile Mapping Point Clouds Based on Improved 3-D Morphological Analysis

Wang

Fan

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In conclusion, point-based segmentation of dense MLS point clouds is possible, as long as efficient strategies are used. Preprocessing steps-especially if the only goal is to segment treesand dimensionality reduction by 2D projection are essential (Yao et al, 2017;Fan et al, 2020;Monnier et al, 2012;Husain and Chandra Vaishya, 2020). For (semantic) segmentation, local features as presented in (Weinmann et al, 2015) are useful, but need a defined neighborhood.…”

Section: Point-based Segmentation Of Urban Treesmentioning

confidence: 99%

“…Before transitioning into voxel space, Yao and Fan (2013) exclude man-made objects, especially facades, exploiting their respective properties when projected to horizontal accumulator spaces at different heights above ground. This idea is similar to (Husain and Chandra Vaishya, 2020), but only used as a prefiltering step here to remove man-made objects. Inspired by the normalized cut approach of Reitberger et al (2009) with ALS data, Yao and Fan apply the same idea to the 3D voxel space of MLS data.…”

Section: Voxel-based Segmentation Of Urban Treesmentioning

confidence: 99%

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A Concept for the Segmentation of Individual Urban Trees From Dense MLS Point Clouds

Hirt

Hoegner

Stilla

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In our daily lives, trees can be seen as the tallest and most noticeable representatives of the plant kingdom. Especially in urban areas, the individual tree is of high significance and responsible for a manifold of positive effects on the environment and residents. In the context of urban tree registers and thus monitoring of urban vegetation, we propose a general concept for the segmentation of trees from 3D point clouds. Mobile Laser Scanning (MLS) is introduced as the preferred sensor. Based on an analysis of earlier work in this field, we gather arguments and methods in order to involve segmentation in the bigger frame of a tree register workflow, including detailed modeling and change detection. Our concept for segmentation is based on a voxel-structure. In a first step, region growing approaches are used for ground removal and rough segmentation. Later, graph-based optimization will separate neighboring trees. For now, only the general concept can be introduced—quantitative analysis and optimization of the steps will follow in future work.

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“…In this work, we focus on the current methods for individual trees detection from MLS. These methods can be roughly divided into three categories, i.e., the normalized cut methods (NCut) [17][18][19][20], the region growing methods [21][22][23][24], the clustering-based methods [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation Guided Coarse-to-Fine Detection of Individual Trees from MLS Point Clouds Based on Treetop Points Extraction and Radius Expansion

Ning

Hou

et al. 2022

Remote Sensing

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Urban trees are vital elements of outdoor scenes via mobile laser scanning (MLS), accurate individual trees detection from disordered, discrete, and high-density MLS is an important basis for the subsequent analysis of city management and planning. However, trees cannot be easily extracted because of the occlusion with other objects in urban scenes. In this work, we propose a coarse-to-fine individual trees detection method from MLS point cloud data (PCD) based on treetop points extraction and radius expansion. Firstly, an improved semantic segmentation deep network based on PointNet is applied to segment tree points from the scanned urban scene, which combining spatial features and dimensional features. Next, through calculating the local maximum, the candidate treetop points are located. In addition, the optimized treetop points are extracted after the tree point projection plane was filtered to locate the candidate treetop points, and a distance rule is used to eliminate the pseudo treetop points then the optimized treetop points are obtained. Finally, after the initial clustering of treetop points and vertical layering of tree points, a top-down layer-by-layer segmentation based on radius expansion to realize the complete individual extraction of trees. The effectiveness of the proposed method is tested and evaluated on five street scenes in point clouds from Oakland outdoor MLS dataset. Furthermore, the proposed method is compared with two existing individual trees segmentation methods. Overall, the precision, recall, and F-score of instance segmentation are 98.33%, 98.33%, and 98.33%, respectively. The results indicate that our method can extract individual trees effectively and robustly in different complex environments.

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An automated approach for street trees detection using mobile laser scanner data

Cited by 6 publications

References 27 publications

An Individual Tree Segmentation Method From Mobile Mapping Point Clouds Based on Improved 3-D Morphological Analysis

An Individual Tree Segmentation Method From Mobile Mapping Point Clouds Based on Improved 3-D Morphological Analysis

A Concept for the Segmentation of Individual Urban Trees From Dense MLS Point Clouds

Semantic Segmentation Guided Coarse-to-Fine Detection of Individual Trees from MLS Point Clouds Based on Treetop Points Extraction and Radius Expansion

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