Recent advances in deep learning have significantly improved 3D semantic segmentation, but most models focus on indoor or terrestrial datasets. Their behavior under real aerial acquisition conditions remains insufficiently explored, and although a few studies have addressed similar scenarios, they differ in dataset design, acquisition conditions, and model selection. To address this gap, we conduct an experimental benchmark evaluating several state-of-the-art architectures on a large-scale aerial LiDAR dataset acquired under operational flight conditions in Navarre, Spain, covering heterogeneous urban, rural, and industrial landscapes. This study compares four representative deep learning models, including KPConv, RandLA-Net, Superpoint Transformer, and Point Transformer V3, across five semantic classes commonly found in airborne surveys, such as ground, vegetation, buildings, and vehicles, highlighting the inherent challenges of class imbalance and geometric variability in aerial data. Results show that all tested models achieve high overall accuracy exceeding 93%, with KPConv attaining the highest mean IoU (78.51%) through consistent performance across classes, particularly on challenging and underrepresented categories. Point Transformer V3 demonstrates superior performance on the underrepresented vehicle class (75.11% IoU), while Superpoint Transformer and RandLA-Net trade off segmentation robustness for computational efficiency.

翻译：深度学习的最新进展显著提升了三维语义分割的性能，但多数模型专注于室内或地面数据集。它们在真实航空采集条件下的表现尚未得到充分探索，尽管已有少量研究涉及类似场景，但这些研究在数据集设计、采集条件和模型选择上存在差异。为填补这一空白，我们开展了一项实验性基准测试，评估了多种最先进架构在西班牙纳瓦拉省实际飞行条件下获取的大规模航空LiDAR数据集上的表现，该数据集覆盖了城市、乡村和工业等异构景观。本研究比较了四种代表性深度学习模型，包括KPConv、RandLA-Net、Superpoint Transformer和Point Transformer V3，涵盖航空测量中常见的五类语义类别（如地面、植被、建筑物和车辆），突显了航空数据中固有的类别不平衡和几何变异性挑战。结果表明，所有测试模型的总体准确率均超过93%，其中KPConv通过各类别的一致性能取得了最高平均交并比（78.51%），尤其在具有挑战性且代表性不足的类别上表现突出。Point Transformer V3在代表性不足的车辆类别上表现优异（IoU为75.11%），而Superpoint Transformer和RandLA-Net则在分割鲁棒性和计算效率之间进行了权衡。