Depth Estimation of Channels Based on Total Station Point Clouds
Bibliographic record
Abstract
Accurate estimation of embedded channel depth in railway tunnels is essential to ensure safe installation, stable operation, and effective maintenance of railway overhead contact network systems. Current methods mainly rely on manual measurement, which is limited by complex environments, technical limitation, high construction accuracy standards, and maintenance and inspection challenges. To address these issues, this paper proposes a novel automated framework to estimate the depth of the embedded channel using high-precision total station point clouds. The framework consists of four modules: point cloud preprocessing, PCA-based orientation adjustment, radian-based channel localization, and GGD-based depth estimation. First, the channel area is roughly extracted from the original tunnel point cloud data using prior knowledge-based filtering. Then, principal component analysis (PCA) is applied to align the extracted channel area for more precise processing. Next, a new radian-based method is proposed to locate the channel position accurately. Finally, the generalized Gaussian distribution (GGD) method fits the channel data and estimates the channel depth. Experiments were conducted using point cloud data acquired from a Topcon GTL-1200 total station. Experimental results demonstrate that the proposed algorithm performs excellently in both channel localization and depth estimation.
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 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".