# Vegetation Removal on 3D Point Cloud Reconstruction of Cut-Slopes Using U-Net
## 1 Introduction  
With the advent of unmanned aerial vehicle (UAV), the 3D reconstruction by photogrammetry has become an useful tool for numerous civil engineering applications including structural modelling [1, 2], structural assessment [3], historical building protecting [4], road construction [5], road maintenance [6], construction progress measurement [7], monitoring and controlling construction site activities [8], land usage investigation [9, 10], etc.  
The 3D reconstruction is also useful for the cut-slope application as a drone can survey a wide range of steep cut-slopes efficiently and effectively without a risk of climbing for field investigators. Photogrammetry provides a 3D point cloud as a comprehensive and quantitative documentation of the slope at a given time, without any subjective assessment by field investigators. The 3D point cloud can provide further geometric and geological information for the slope stability analysis [11].  
Ideally, engineers want to monitor long-term changes of cut-slopes. In theory, this can be done by inspecting the difference between two 3D point clouds temporarily apart, but it is often the case the difference is submerged by seasonal changes of vegetation on the cut-slopes. In order to make such technology realisable, it is important to remove vegetation from the 3D point clouds.  
There are three different approaches found in the literature for vegetation removal depending on how each point in the point cloud is classified: (1) the colour index approach [12, 13]; (2) the planar estimation approach [14, 15, 16]; or (3) the machine learning based 3D point cloud segmentation approach [17, 18].  
The colour index approach is simple to use and works reasonably for a specific scene with tuned parameters, but has a limited performance when the colour of a non-vegetation is close to the colour of vegetation. The planar estimation approach showed a limited performance on slopes with complex surface geometry [14]. Considering the planar estimation approach's working principle that vegetation is separated from ground surface by distance, it is expected to work reasonably well for tall vegetation, but not for short vegetation. The comparative study [19] showed even the best performance method was not able to remove vegetation completely. The machine learning based 3D point cloud segmentation approach is relatively new, but promising with an expectation of high-performance when trained with a large dataset [17; 18]. Pix4 Dmapper provides a machine learning based point cloud classifier for a dense point cloud reconstructed by itself [20]. The active research for 3D segmentation in computer science, medical imaging, etc. [21; 22; 23; 24] is likely to benefit the 3D vegetation removal approach. However, the requirement for a large 3D dataset is a practical problem in the development [17].  
For a high-performance vegetation removal with less effort, this study proposes a new approach combining the deep learning-based 2D image segmentation and photogrammetry. The colour index method is employed to reduce the effort in preparing a 2D segmented image dataset.  
The paper is organised as following: introduction to the cut-slope studied in this work (Section 2), methodology of the proposed 3D vegetation removal approach (Section 3), performance of the proposed approach on the cut-slope (Section 4), and the conclusion.