SOURCE-LINKED INTELLIGENCE
Learning-Based 3D Reconstruction of Power Networks from Aerial Point Clouds
This paper presents an end-to-end framework for reconstructing overhead power utility network topology and extracting span-level physical metadata from large-scale aerial LiDAR. The pipeline begins with semantic segmentation of the input point cloud using an improved KPConv-based model, in which data sampling and loss functions are adapted to emphasize pole and conductor (wire) classes. Network topology inference then proceeds in two stages: (i) pole instances are obtained by clustering pole-class points and validating candidates using geometric criteria, including height and verticality estim
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-20T22:43:42.000Z
First collected: 2026-09-23T09:51:33.063Z. This is not the publication date.