  Recent developments in data acquisition technology allow us to collect 3D
texture meshes quickly. Those can help us understand and analyse the urban
environment, and as a consequence are useful for several applications like
spatial analysis and urban planning. Semantic segmentation of texture meshes
through deep learning methods can enhance this understanding, but it requires a
lot of labelled data. This paper introduces a new benchmark dataset of semantic
urban meshes, a novel semi-automatic annotation framework, and an open-source
annotation tool for 3D meshes. In particular, our dataset covers about 4 km2 in
Helsinki (Finland), with six classes, and we estimate that we save about 600
hours of labelling work using our annotation framework, which includes initial
segmentation and interactive refinement. 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).
