# Vegetation Extraction from Airborne Laser Scanning Data of Urban Plots Based on Point Cloud Neighborhood Features

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Data Acquisition and Preprocessing

#### 2.2. Research Methods

#### 2.2.1. Point Cloud Planar Features and Discrete Features

_{i}(i = 1, 2, 3... n) be a point in the point cloud set C. Then, the covariance matrix composed of p

_{i}and the points in its R-neighborhood can be described as follows [24].

_{i}, $\stackrel{-}{p}$ represents the geometric center of the point set in the neighborhood, j represents the number of eigenvalues and eigenvectors, j = 3 and e

_{j}and λ

_{j}represent the corresponding eigenvectors and eigenvalues, where λ

_{0}> λ

_{1}> λ

_{2.}

#### 2.2.2. Vegetation Extraction under the Building Point Cloud Constraints

_{1}(x

_{1}, y

_{1}, z

_{1}) and p

_{2}(x

_{2}, y

_{2}, z

_{2}) as two points in the three-dimensional space, the Euclidean distance d between these two points is calculated as follows.

_{d}is set for clustering according to the average distance between vegetation and buildings in the experimental data. The number and percentage of points in each category are counted, the few types of point clouds with the least percentage are removed and the rest is considered the vegetation point cloud.

#### 2.2.3. Accuracy Evaluation

## 3. Results

#### 3.1. Influence of K-Neighborhood and R-Neighborhood on Point Cloud Features

#### 3.2. Rough Extraction of Vegetation and Building Point Cloud

#### 3.3. Building Boundary Point Cloud Filtering in the Vegetation Point Cloud

#### 3.4. Accurate Extraction of Vegetation Point Cloud

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**Technology roadmap. (

**I**) Rough extraction of vegetation point cloud and building point cloud; (

**II**) Removing the building boundary point cloud in the vegetation point cloud; (

**III**) Accurate extraction of vegetation point cloud.

**Figure 4.**The feature extraction results of K-neighborhood and R-neighborhood of plot 1. (

**a**) Characteristic of linearity of K-nearest neighbors; (

**b**) Characteristic of planarity of K-nearest neighbors; (

**c**) Characteristic of dispersion of K-nearest neighbors; (

**d**) Characteristic of linearity of R-nearest neighbors; (

**e**) Characteristic of planarity of R-nearest neighbors; (

**f**) Characteristic of planarity of R-nearest neighbors.

**Figure 5.**The feature extraction results of K-neighborhood and R-neighborhood of plot 2. (

**a**) Characteristic of linearity of K-nearest neighbors; (

**b**) Characteristic of planarity of K-nearest neighbors; (

**c**) Characteristic of dispersion of K-nearest neighbors; (

**d**) Characteristic of linearity of R-nearest neighbors; (

**e**) Characteristic of planarity of R-nearest neighbors; (

**f**) Characteristic of planarity of R-nearest neighbors.

**Figure 6.**Vegetation extraction results at different R-neighborhood scales in plot 1. (

**a**) R = 0.2 m; (

**b**) R = 0.4 m; (

**c**) R = 0.6 m; (

**d**) R = 0.8 m; (

**e**) R = 1 m.

**Figure 7.**Vegetation extraction results at different R-neighborhood scales in plot 2. (

**a**) R = 0.2 m; (

**b**) R = 0.4 m; (

**c**) R = 0.6 m; (

**d**) R = 0.8 m; (

**e**) R = 1 m.

**Figure 8.**Building point cloud extracted results based on plane feature extraction. (

**a**) Plot 1; (

**b**) Plot 2.

**Figure 9.**Accurate extraction results of the building point cloud. (

**a**) Cluster result of plot 1; (

**b**) Building extraction result of plot 1; (

**c**) Cluster result of plot 2; (

**d**) Building extraction result of plot 2.

**Figure 10.**The results of the vegetation point clouds of the two plots after filtering out the building boundary point clouds. (

**a**) Vegetation extracted using discrete feature of plot 1; (

**b**) Result of filtering the building boundary of plot 1; (

**c**) Vegetation extracted using discrete feature of plot 2; (

**d**) Result of filtering the building boundary of plot 2.

**Figure 11.**Vegetation extraction results of plot 1 at different r scales. (

**a**) r = 0.5 m; (

**b**) r = 1 m; (

**c**) r = 1.5 m; (

**d**) r = 2 m.

**Figure 12.**Vegetation extraction results of plot 2 at different r scales. (

**a**) r = 0.5 m; (

**b**) r = 1 m; (

**c**) r = 1.5 m; (

**d**) r = 2 m.

**Figure 13.**The final accurate extraction results of vegetation in two plots. (

**a**) Original data of plot 1; (

**b**) Final vegetation extraction result of plot 1; (

**c**) Original data of plot 2; (

**d**) Final vegetation extraction result of plot 2.

r (m) | TP | FP | FN | Recall | Precision |
---|---|---|---|---|---|

0.5 | 536,219 | 3797 | 131,008 | 80.37% | 99.30% |

1 | 615,104 | 7859 | 52,123 | 92.19% | 98.74% |

1.5 | 633,916 | 14,262 | 33,311 | 95.01% | 97.80% |

2 | 641,247 | 21,278 | 25,980 | 96.11% | 96.79% |

r (m) | TP | FP | FN | Recall | Precision |
---|---|---|---|---|---|

0.5 | 375,852 | 3508 | 78,619 | 82.70% | 99.08% |

1 | 428,581 | 5530 | 25,890 | 94.30% | 98.73% |

1.5 | 438,518 | 9065 | 15,953 | 96.49% | 97.97% |

2 | 442,108 | 14,780 | 12,363 | 97.28% | 96.77% |

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## Share and Cite

**MDPI and ACS Style**

Zhang, J.; Wang, J.; Ma, W.; Deng, Y.; Pan, J.; Li, J. Vegetation Extraction from Airborne Laser Scanning Data of Urban Plots Based on Point Cloud Neighborhood Features. *Forests* **2023**, *14*, 691.
https://doi.org/10.3390/f14040691

**AMA Style**

Zhang J, Wang J, Ma W, Deng Y, Pan J, Li J. Vegetation Extraction from Airborne Laser Scanning Data of Urban Plots Based on Point Cloud Neighborhood Features. *Forests*. 2023; 14(4):691.
https://doi.org/10.3390/f14040691

**Chicago/Turabian Style**

Zhang, Jianpeng, Jinliang Wang, Weifeng Ma, Yuncheng Deng, Jiya Pan, and Jie Li. 2023. "Vegetation Extraction from Airborne Laser Scanning Data of Urban Plots Based on Point Cloud Neighborhood Features" *Forests* 14, no. 4: 691.
https://doi.org/10.3390/f14040691