In the practical and professional work of classifying airborne laser scanning (ALS) point clouds, there are nowadays numerous methods and software applications available that are able to separate the points into a few basic categories and do so with a known and consistent quality. Further refinement of the classes then requires either manual or semi-automatic work, or the use of supervised machine learning algorithms. In using supervised machine learning, e.g. Deep Learning neural networks, however, there is a significant chance that they will not maintain the approved quality of an existing classification. In this study, we therefore evaluate the application of two neural networks, PointNet++ and KPConv, and propose to integrate prior knowledge from a pre-existing classification in the form of height above ground and an encoding of the already available labels as additional per-point input features. Our experiments show that such an approach can improve the quality of the 3D classification results by 6% to 10% in mean intersection over union (mIoU) depending on the respective network, but it also cannot completely avoid the aforementioned problems.