  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.
