PAN-SUNET: UTILITY CORRIDOR UNDERSTANDING USING SPATIAL LAYOUT CONSISTENCY
Bibliographic record
Abstract
Abstract. The article addresses the need for a dependable and efficient computer vision system to examine utility networks with minimal human intervention, given the deteriorating state of these networks. To classify the dense and irregular point clouds obtained from the airborne laser terrain mapping (ALTM) system, which is used for data collection, we suggest a deep learning network named Panoptic-Semantic Utility Network (Pan-SUNet). The proposed network incorporates three networks to achieve voxel-based semantic segmentation and 3D object detection of the point clouds at various resolutions, including object categories in three dimensions, and predicts two-dimensional regional labels to differentiate utility and corridor regions from non-corridor regions. The network also ensures spatial layout consistency in the prediction of the voxel-based 3D network using regional segmentation. By testing the proposed approach on 67 km2 of utility corridor data with an average density of 5 pts/m2, the paper demonstrates the effectiveness of the technique. The proposed network outperforms the state-of-the-art baseline network, achieving an F1 score of 94% for the pylon class, 99% for the ground class, 99% for the vegetation class, and 99% for the powerline class. It also shows high performance for 3D object detection for pylon and span achieving average precision of 99% and 92% respectively.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".