Semantic labeling is an essential but challenging task when interpreting point clouds of 3D scenes. As a core step for interpretation, semantic labeling is to annotate every point in the point cloud with a label of semantic meaning, which plays a significant role in many point cloud related applications. Especially for airborne laser scanning (ALS) point clouds, precise annotations can considerably broaden its use in various applications. However, accurate and efficient semantic labeling is still a challenging task, due to the sensor noise, complex object structures, incomplete points, and uneven point densities. In this work, we propose a novel neural network focusing on semantic labeling of ALS point clouds, which investigates the importance of long-range spatial and channelwise relations and is termed as global relation-aware attentional network (GraNet). GraNet first learns local geometric description and local dependencies using a local spatial discrepancy attention convolution module (LoSDA). In LoSDA, the orientation information, spatial distribution, and elevation differences are fully considered by stacking several local spatial geometric learning modules and the local dependencies are embedded by using an attention pooling module. Then, a global relation-aware attention module (GRA), consisting of a spatial relation-aware attention module (SRA) and a channel relation aware attention module (CRA), are investigated to further learn the global spatial and channel-wise relationship between any spatial positions and feature vectors. The aforementioned two important modules are embedded in the multi-scale network architecture to further consider scale changes in large urban areas. We conducted comprehensive experiments on two ALS point cloud datasets to evaluate the performance of our proposed framework. The results show that our method can achieve higher classification accuracy compared with other commonly used advanced classification methods. The overall accuracy (OA) of our method on the ISPRS benchmark dataset can be improved to 84.5 % to classify nine semantic classes, with an average F 1 measure (AvgF 1 ) of 73.5 %, which outperforms other strategies. Besides, experiments were conducted using a new ALS point cloud dataset covering highly dense urban areas.