  Stack interchanges are essential components of transportation systems. Mobile
laser scanning (MLS) systems have been widely used in road infrastructure
mapping, but accurate mapping of complicated multi-layer stack interchanges are
still challenging. This study examined the point clouds collected by a new
Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) system to
perform the semantic segmentation task of a stack interchange. An end-to-end
supervised 3D deep learning framework was proposed to classify the point
clouds. The proposed method has proven to capture 3D features in complicated
interchange scenarios with stacked convolution and the result achieved over 93%
classification accuracy. In addition, the new low-cost semi-solid-state LiDAR
sensor Livox Mid-40 featuring a incommensurable rosette scanning pattern has
demonstrated its potential in high-definition urban mapping.
