GKCAE: A Graph-Attention-based Encoder for Fine-Grained Semantic Segmentation of High-Voltage Transmission Corridors Scenario LiDAR Data

Su Zhang^1*Haibo Liu²Jingguo Rong²Yaping Zhang²

• ¹State Grid Economic and Technological Research Institute Co Ltd, 821961, Beijing, China
• ²State Grid Economic and Technological Research Institute Co Ltd, Beijing, China

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 real-world 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.