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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1649203/full |
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| author | Su Zhang Haibo Liu Jingguo Rong Yaping Zhang |
| author_facet | Su Zhang Haibo Liu Jingguo Rong Yaping Zhang |
| author_sort | Su Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bf59bb1bcf3b4f7bb17b0eebd8bdd071 |
| institution | Kabale University |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-bf59bb1bcf3b4f7bb17b0eebd8bdd0712025-08-21T05:27:28ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-08-011310.3389/feart.2025.16492031649203GKCAE: A graph-attention-based encoder for fine-grained semantic segmentation of high-voltage transmission corridors scenario LiDAR dataSu ZhangHaibo LiuJingguo RongYaping ZhangAccurate 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.https://www.frontiersin.org/articles/10.3389/feart.2025.1649203/fullALS point cloudssemantic segmentationgraph edge convolutionhigh-voltage transmission corridorsdeep learning |
| spellingShingle | Su Zhang Haibo Liu Jingguo Rong Yaping Zhang GKCAE: A graph-attention-based encoder for fine-grained semantic segmentation of high-voltage transmission corridors scenario LiDAR data Frontiers in Earth Science ALS point clouds semantic segmentation graph edge convolution high-voltage transmission corridors deep learning |
| title | GKCAE: A graph-attention-based encoder for fine-grained semantic segmentation of high-voltage transmission corridors scenario LiDAR data |
| title_full | GKCAE: A graph-attention-based encoder for fine-grained semantic segmentation of high-voltage transmission corridors scenario LiDAR data |
| title_fullStr | GKCAE: A graph-attention-based encoder for fine-grained semantic segmentation of high-voltage transmission corridors scenario LiDAR data |
| title_full_unstemmed | GKCAE: A graph-attention-based encoder for fine-grained semantic segmentation of high-voltage transmission corridors scenario LiDAR data |
| title_short | GKCAE: A graph-attention-based encoder for fine-grained semantic segmentation of high-voltage transmission corridors scenario LiDAR data |
| title_sort | gkcae a graph attention based encoder for fine grained semantic segmentation of high voltage transmission corridors scenario lidar data |
| topic | ALS point clouds semantic segmentation graph edge convolution high-voltage transmission corridors deep learning |
| url | https://www.frontiersin.org/articles/10.3389/feart.2025.1649203/full |
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