GKCAE: A graph-attention-based encoder for fine-grained semantic segmentation of high-voltage transmission corridors scenario LiDAR data

GKCAE: A graph-attention-based encoder for fine-grained semantic segmentation of high-voltage transmission corridors scenario LiDAR data

Accurate semantic segmentation of airborne LiDAR point clouds is essential for the intelligent inspection and maintenance of high-voltage transmission infrastructure. While existing methods predominantly focus on major structural components such as towers and conductors, they often fail to address t...

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Bibliographic Details
Main Authors: Su Zhang, Haibo Liu, Jingguo Rong, Yaping Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Earth Science
Subjects:
ALS point clouds
semantic segmentation
graph edge convolution
high-voltage transmission corridors
deep learning
Online Access:https://www.frontiersin.org/articles/10.3389/feart.2025.1649203/full
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Summary:Accurate semantic segmentation of airborne LiDAR point clouds is essential for the intelligent inspection and maintenance of high-voltage transmission infrastructure. While existing methods predominantly focus on major structural components such as towers and conductors, they often fail to address the fine-grained segmentation of smaller yet critical elements, including ground wires, crossing lines, and insulators. To tackle this limitation, we propose a novel network architecture—Graph-Kernel Convolution Attention Encoder (GKCAE)—designed for multi-class, fine-grained semantic segmentation of transmission corridor point clouds. GKCAE first captures local geometric features using Kernel Point Convolution, and then models inter-class spatial relationships through Graph Edge-Conditioned Convolution to incorporate global contextual information. Additionally, a Channel-Spatial Attention Module is introduced to enhance point-level feature representations, particularly for small or geometrically similar classes. Experiments conducted on three realworld transmission corridor datasets demonstrate that our method achieves a mean Intersection over Union (mIoU) of 81.93% and an Overall Accuracy (OA) of 94.1%, outperforming existing state-of-the-art approaches.
ISSN:2296-6463

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