Airport ground operations must be safe and should avoid capacity backlogs. To that end, the availability of reliable surveillance data capturing important semantic information about the local traffic situation and the operating conditions on the apron and the maneuvering area are essential. Along those lines, LiDAR sensors combined with computer vision algorithms for semantic scene understanding were recently identified to offer a cost-effective, noncooperative surveillance solution that is expected to contribute to the operational goals mentioned above. However little work exists dealing with fast and accurate LiDAR semantic segmentation of such environments. This is partly due to the fact, that state-of-the-art algorithms in LiDAR semantic segmentation heavily rely on large-scale data sets with fine-grained labels. Although some hand-labeled data sets are publicly available, the point-wise annotation of 3D point clouds requires painstakingly work that is extremely cumbersome. Consequently, we propose a simulation-based approach to generate synthetic training data of the apron using a virtual airport environment that integrates a LiDAR sensor model. In this way, arbitrary scenarios captured under different operational conditions including static objects and moving aircraft provide labeled point data. This way, we trained and tested successfully a LiDAR semantic segmentation model emphasizing on aircraft approaching/leaving the gate after arrival/departure, thin structures (poles), airport buildings, and ground-plane. The developed technique provides an important baseline for the expected performance of the trained model on real data. We believe that the resulting framework provides additional visual cues capturing relevant semantic information that potentially assist the controller in complex situations.