Robust Utility Networks Registration with Powerline Segmentation on Noisy Point Clouds

The full pipeline: raw or pre-classified aerial LiDAR in, a per-pole and per-conductor asset record out. The trick is a topology network trained to work even when the point labels are bad, and a per-span conductor separator that picks between a learned and a geometric expert.

Pole detection ≥ 0.91 · conductor assignment 99.8% precision · 12–18 cm on endpoint height and sag, across 0.4 to 193 points per m².

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Aerial LiDAR has become the standard sensor for surveying overhead utility networks, yet converting a survey into a structured per-conductor digital asset record remains largely manual and time consuming. We present an end-to-end framework that converts aerial LiDAR surveys, raw or pre-classified, into structured, per-pole and per-conductor digital utility asset records. Network topology is reconstructed in a single forward pass by a dual-head ResUNet-FPN over four rasterised DSM channels, two class-dependent and two class-agnostic, regularised by ClassificationChannelDropout, a semantically structured masking scheme that forces the encoder to commit to the classification-invariant pathway and lifts pole detection by +7.1% (vs. the same model without the mask) on degraded inputs at no measurable cost on clean ones. For each reconstructed span, individual conductors are recovered by combining a PointNet-style learned segmenter trained entirely on synthetic spans with a training-free geometric pipeline (PCA alignment, catenary straightening, HDBSCAN), routed by a mixture-of-experts gate. Continuous catenary fits over the resulting per-point assignments make it feasible to extract endpoint heights, sag, clearance, length and inter-wire spacing as one-line queries. Across aerial LiDAR corpora spanning 0.4–193 pts/m² and clean-to-degraded classification, the system delivers PDet ≥ 0.91 (pole detection IoU) and WCLD ≥ 0.92 (wire detection score) on topology, 99.8% per-point precision on conductor assignment under consensus gating, and 12–18 cm MAE on endpoint and sag attributes.