Feature learning of airborne laser scanning (ALS) point clouds is challenged by both the limited annotated samples and imbalanced class distribution. An intuitive way involves pretraining on a well-annotated source dataset and fine-tuning on a limited target dataset. However, cross-domain challenges such as heterogeneous point cloud density, varying terrain features, and inconsistent object categories complicate transfer learning for 3-D land cover classification. In this article, we address these issues by separating the cross-domain ALS point cloud semantic segmentation into two subsequent subtasks, i.e., the cross-domain transfer learning subtask and the intradomain class-incremental learning subtask, and we use a well-annotated photogrammetric point cloud dataset as the source dataset. To mitigate domain discrepancies, the first subtask employs domain adversarial training to learn from base categories that are shared between source and target datasets. Then, the second subtask incrementally learns new categories that are specific within the target dataset using an incremental feature-semantic distillation module and a semantic adversarial learning module while retaining base category knowledge. Experimental results evaluated on three ALS point cloud datasets (ISPRS, DALES, and H3D) with different semantics show state-of-the-art cross-domain performance with few labeled samples. Compared with few-shot learning methods, our method shows promising generalization ability particularly on domain-specific categories, greatly alleviating the dependence on ALS point cloud annotations.