# Vegetation Removal on 3D Point Cloud Reconstruction of Cut-Slopes Using U-Net
## 4 Result of the Proposed Method on Doam Dam  
This section presented (1) the vegetation removed results by the proposed method in comparison with two existing vegetation removed methods on Doam Dam's point clouds, (2) the negative effect of vegetation on long-term cut-slope monitoring, and (3) a possibility of long-term cut-slope monitoring realised by the proposed vegetation removed method. The two existing methods used were (1) Cloth Simulation Filter [14], available in CloudCompare [33], and (2) dense point cloud classification [20], available in Pix4 Dmapper [34]. Doam Dam's point clouds used in this study were reconstructed from two sets of photos taken at two different times, one at winter (January 2018) and the other at summer (September 2017). Each image set consists of 1305 images and was processed by COLMAP. The proposed method was applied to the two point clouds and the results were shown in Figures 16-18. Figure 16 shows (a) the original 3D point cloud in winter, and (b) the point cloud classification result by Pix4 Dmapper, (c) vegetation removed point cloud by the Cloth Simulation Filter, and (d) the vegetation removed point cloud by the proposed method. The point cloud classification by Pix4 Dmapper showed a poor performance: many parts on the slope were classified as \"building\", and the large dry vegetation patch on the left of the cut-slope was classified as a mixture of Ground and high vegetation, rather than vegetation. This may indicate that the machine learning model of Pix4D was not trained extensively for cut-slopes. The vegetation removed point cloud by the Cloth Simulation Filter (the options used were the cloth resolution of 0.1 m and the classification threshold of 0.1 m) showed the trees on the side of the road at the bottom were removed significantly. However, a large portion of the point cloud was still brown due to unremoved dry grasses and bushes on the slope. Considering the working principle of the Cloth Simulation Filter, this was an expected result. The vegetation removed result by the proposed method showed that the dry vegetation was successfully removed from the point cloud. Figure 16: _Cont._Figure 16: Three-dimensional (3D) point clouds of Doam Dam in January 2018: (**a**) with vegetation, (**b**) point cloud classification by Pix4 Dmapper, (**c**) vegetation removed by the Cloth Simulation Filter, and (**d**) vegetation removed by the proposed method. Figure 17 shows (a) the original 3D point cloud in summer, and (b) the point cloud classification result by Pix4 Dmapper, (c) vegetation removed point cloud by the Cloth Simulation Filter, and (d) the vegetation removed point cloud by the proposed method. The point cloud classification by Pix4 Dmapper showed a poor performance again: most parts on the slope were classified as \"high vegetation\", and \"ground\" point clouds were relatively smaller than \"high vegetation\". However, it was interesting to observe that \"road surface\" and two utility poles (\"human made object\") were properly classified. Again, this may indicate that the machine learning model of Pix4D was not trained extensively for vegetation and non-vegetation on cut-slopes. The vegetation removed point cloud by the Cloth Simulation Filter showed a similar result with the winter one. It removed the trees on the side of the road at the bottom well. However, a large portion of the point cloud was still green due to unremoved fresh grasses and bushes on the slope. The vegetation removed result by the proposed method showed that the fresh vegetation was successfully removed from the point cloud. Figure 17: _Cont._  
Figure 18 shows zoom-in views of Figures 16 and 17 for the same region on the cut-slope. Excellent vegetation removal performance was found for both fresh (green) and dry (brown) vegetation in comparison with the two existing methods. For a purpose of long-term cut-slope monitoring, a geometric distance metric must be used to calculate distance between two point clouds.