Before revision:  Furthermore, we compare the performance of several representative 3D semantic segmentation methods on our annotated dataset. The results show our initial segmentation outperforms other methods and achieves an overall accuracy of 93.0\% and mIoU of 66.2\% with less training time compared to other deep learning methods. We also evaluate the effect of the input training data, which shows that our method only requires about 7\% (which covers about 0.23 km2) to approach robust and adequate results whereas KPConv needs at least 33\% (which covers about 1.0 km2) .
After revision:  Furthermore, we compare the performance of several representative 3D semantic segmentation methods on the new benchmark dataset. Other researchers can use our results to train their networks: the dataset is publicly available, and the annotation tool is released as open-source .