AUTOMATIC POWERLINE SCENE CLASSIFICATION AND RECONSTRUCTION USING AIRBORNE LIDAR DATA
Bibliographic record
Abstract
Abstract. This study aims to introduce new methods for classifying key features (power lines, pylons, and buildings) comprising utility corridor scene using airborne LiDAR data and modelling power lines in 3D object space. The proposed approach starts from PL scene segmentation using Markov Random Field (MRF), which emphasizes on the roles of spatial context of linear and planar features as in a graphical model. The MRF classifier identifies power line features from linear features extracted from given corridor scenes. The non-power line objects are then investigated in a planar space to sub-classify them into building and non-building class. Based on the classification results, precise localization of individual pylons is conducted through investigating a prior knowledge of contextual relations between power line and pylon. Once the pylon localization is accomplished, a power line span is identified, within which power lines are modelled with catenary curve models in 3D. Once a local catenary curve model is established, this initial model progressively extends to capture entire power line points by adopting model hypothesis and verification. The model parameters are adjusted using a stochastic non-linear square method for producing 3D power line models. An evaluation of the proposed approach is performed over an urban PL corridor area that includes a complex PL scene.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".