  Due to the aged nature of much of the utility network infrastructure,
developing a robust and trustworthy computer vision system capable of
inspecting it with minimal human intervention has attracted considerable
research attention. The airborne laser terrain mapping (ALTM) system quickly
becomes the central data collection system among the numerous available
sensors. Its ability to penetrate foliage with high-powered energy provides
wide coverage and achieves survey-grade ranging accuracy. However, the
post-data acquisition process for classifying the ALTM's dense and irregular
point clouds is a critical bottleneck that must be addressed to improve
efficiency and accuracy. We introduce a novel deep convolutional neural network
(DCNN) technique for achieving voxel-based semantic segmentation of the ALTM's
point clouds. The suggested deep learning method, Semantic Utility Network
(SUNet) is a multi-dimensional and multi-resolution network. SUNet combines two
networks: one classifies point clouds at multi-resolution with object
categories in three dimensions and another predicts two-dimensional regional
labels distinguishing corridor regions from non-corridors. A significant
innovation of the SUNet is that it imposes spatial layout consistency on the
outcomes of voxel-based and regional segmentation results. The proposed
multi-dimensional DCNN combines hierarchical context for spatial layout
embedding with a coarse-to-fine strategy. We conducted a comprehensive ablation
study to test SUNet's performance using 67 km x 67 km of utility corridor data
at a density of 5pp/m2. Our experiments demonstrated that SUNet's spatial
layout consistency and a multi-resolution feature aggregation could
significantly improve performance, outperforming the SOTA baseline network and
achieving a good F1 score for pylon 89%, ground 99%, vegetation 99% and
powerline 98% classes.
