Multi-Scale Hierarchical CRF for Railway Electrification Asset Classification From Mobile Laser Scanning Data
Bibliographic record
Abstract
A network of railway infrastructure is one of the most critical infrastructure assets for supporting the national economy and sustainable mobility. Safe and reliable maintenance of railway infrastructure is critical to ensure that rail systems run safely and punctually. Such maintenance requires regular surveying of railway assets, which typically relies on time-consuming and error-prone labor-centric visual inspection. In this paper, we propose a novel supervised method for automatically classifying electrification assets of railway networks using mobile laser scanning data. A hierarchical Conditional Random Field (CRF) was investigated in order to apply both smoothness constraint and spatial regularities, to improve the classification result made by local supervised classifiers. We use a multi-scale line representation of original data, which implicitly combines object geometry cues and makes computation efficient. Our approach focuses on learning the spatial regularities at multiple representation scales to thoroughly understand the railway electrification scene. The spatial regularities are formulated as relative spatial location in a middle range for different line primitive scales and relative displacement in a full range for the final coarsest line primitive scale. The experiment shows that learnt spatial regularities at full range with multi-scales can outperform the model with spatial regularities at limited local ranges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".