Urban power line extraction from mobile LiDAR point clouds based on local line-plane seperation model and global refinement
Bibliographic record
Abstract
Abstract Power line extraction from three-dimensional (3D) point clouds is a key step for power inspection. However, the power line is intertwined and covered with trees and other objects in the urban area, which brings a tremendous challenge to its extraction. This article proposed a local-to-global method to extract power lines from mobile Light Detection and Ranging point clouds based on multiple characteristics. Firstly, initial point clouds clusters are selected through data preprocessing. Secondly, given the linearity and independence of the power line, we innovatively design a local line-plane separation modeling to divide the point clouds into high-confidence power line points, non-power line points, and unconfirmed points. Then, a novel global energy function is designed for the graph cut model according to the long linear and short radius characteristics of the power line. The proposed method has been tested on Toronto 3D and WuHan Avenue datasets. The average Precision , Recall , and F1score can reach up to 0.92, 0.73, and 0.79 respectively, which is superior to state-of-the-art approaches. The result demonstrates the efficacy and robustness of our proposed approach in extracting power lines within urban environments using a mobile laser scanning system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".