Mobile mapping is an application field of ever-increasing relevance. Data of the surrounding environment is typically captured using combinations of LiDAR systems and cameras. The large amounts of measurement data are then processed and interpreted, which is often done automated using neural networks. For the evaluation the data of the LiDAR and the cameras needs to be fused, which requires a reliable calibration of the sensors. Segmentation solemnly on the LiDAR data drastically decreases the amount of data and makes the complex data fusion process obsolete but on the other hand often performs poorly due to the lack of information about the surface remission properties. The work at hand evaluates the effect of a novel multispectral LiDAR system on automated semantic segmentation of 3D-point clouds to overcome this downside. Besides the presentation of the multispectral LiDAR system and its implementation on a mobile mapping vehicle, the point cloud processing and the training of the CNN are described in detail. The results show a significant increase in the mIoU when using the additional information from the multispectral channel compared to just 3D and intensity information. The impact on the IoU was found to be strongly dependent on the class.