Developing Computer Vison-based Digital Twin for Vegetation Management Near Power Distribution Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid development of digital twin technology has opened new avenues for infrastructure management, particularly for addressing vegetation encroachment risks near power lines.This paper builds upon our previous work in LiDAR-based proximity detection by proposing a framework for creating digital twin for vegetation management near power distribution networks.The framework leverages the RandLA-Net model for semantic segmentation of power lines, poles, and vegetation followed by clustering and rule-based thresholding for data refinement.Detecting vegetation encroachment is achieved through KDTree-based spatial analysis, ensuring efficient identification of risk zones.The segmented and processed point cloud data is then transformed into detailed 3D models, forming the basis of the digital twin, which can be enhanced in the future by adding advanced semantic attributes and predictive tree growth models, enabling proactive vegetation management.The methodology is demonstrated through a case study, highlighting its potential to enhance operational efficiency and the resilience of power distribution networks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it