Interpretation of Airborne Laser Scanning (ALS) point clouds is a critical procedure for producing various geo-information products like 3D city models, digital terrain models and land use maps. In this paper, we present a local and global encoder network (LGENet) for semantic segmentation of ALS point clouds. Adapting the KPConv network, we first extract features by both 2D and 3D point convolutions to allow the network to learn more representative local geometry. Then global encoders are used in the network to exploit contextual information at the object and point level. We design a segment-based Edge Conditioned Convolution to encode the global context between segments. We apply a spatial-channel attention module at the end of the network, which not only captures the global interdependencies between points but also models interactions between channels. We evaluate our method on two ALS datasets namely, the ISPRS benchmark dataset and DCF2019 dataset. For the ISPRS benchmark dataset, our model achieves state-of-the-art results with an overall accuracy of 0.845 and an average F1 score of 0.737. With regards to the DFC2019 dataset, our proposed network achieves an overall accuracy of 0.984 and an average F1 score of 0.834.

机载激光扫描（ALS）点云的解释是生产各种地理信息产品（如3D城市模型，数字地形模型和土地使用图）的关键过程。在本文中，我们提出了用于ALS点云语义分割的本地和全局编码器网络（LGENet）。适应KPConv网络，我们首先通过2D和3D点卷积提取特征，以使网络学习更多具有代表性的局部几何形状。然后，在网络中使用全局编码器在对象和点级别利用上下文信息。我们设计了一个基于段的边缘条件卷积，以对段之间的全局上下文进行编码。我们在网络的末端应用了一个空间通道注意模块，它不仅捕获了点之间的全局相互依赖性，而且还对通道之间的交互进行了建模。我们在两个ALS数据集（即ISPRS基准数据集和DCF2019数据集）上评估了我们的方法。对于ISPRS基准数据集，我们的模型获得了最先进的结果，总体准确度为0.845，平均F1得分为0.737。关于DFC2019数据集，我们提出的网络实现了0.984的整体准确性和0.834的平均F1分数。