LineShield - A Generalized LiDAR Pipeline for Automated Vegetation Encroachment Detection on Powerlines
Bibliographic record
Abstract
Vegetation encroachment on the powerline poses significant risks to the reliability and safety of the power infrastructure. Many LiDAR-Based methods have tried to address this problem, yet these methods lack scalability and generality across different LiDAR collection methods with varying resolutions and data sizes. This paper presents LineShield, a generalized LiDAR-based pipeline for automated vegetation encroachment detection on powerlines. The pipeline addresses these challenges on both airborne and mobile LiDAR datasets. Key components: clustering with DBSCAN for accurate powerline segmentation, PCA-based alignment for standardizing orientation, sliding window traversal for efficient processing of large datasets, voxel downsampling for reducing data complexity, and proximity-based severity classification for prioritizing interventions. Experimental results demonstrate the pipeline’s adaptability across three datasets, namely, ECLAIR, DALES, and Toronto-3D, achieving up to 97.4% detection accuracy and maintaining performance in both urban and rural environments. The approach also provides detailed reporting to help maintenance teams prioritize interventions.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".