Identifying Rockfall Hazards in White Canyon, British Columbia: An Approach to the Development and Analysis of Dense Point Clouds from Different Remote Sensors
Bibliographic record
Abstract
Due to the numerous extensive rock cuts required through steep topography, rockfalls are a common risk to which the Canadian National Railway (CN) and Canadian Pacific Railway (CP) rail corridors in Western Canada are exposed. Rockfall hazards on railway corridors create risk of derailment which can result in damage to property or the environment and cause injury or loss of life. There is interest in understanding the location and severity of such hazards so that management strategies can be implemented. Previous studies at White Canyon near Lytton, British Columbia have demonstrated how remote sensing data collection such as terrestrial laser scanning (TLS) and photogrammetry has increased knowledge of the magnitude and frequency distribution of rockfalls. However, there are some data limitations preventing analysis of all sectors of the canyon, largely due to complex geometry of the slope which includes vertical spires of rock protruding from the slope. This study aims to improve the understanding of rockfalls through the integration of TLS and UAV-SfM point clouds. In addition, new terrestrial monitoring methods using particle tracking on sequential photographs generates results at higher temporal resolution with less computational expense for the near real-time evaluation of slopes. The research presented herein has several objectives: (1) Generate dense point clouds from different patterns in the flight plans of a UAV to collect data in remote areas; (2) Combine point clouds from TLS and UAV-SfM to obtain a complete model of the complex geomorphology of the spires in White Canyon; (3) Perform change detection calculations in integrated UAV-SfM and TLS point clouds and filter the change detected; and (4) Implement low-cost, near real-time frequency terrestrial photomonitoring to collect data from the slope and spires.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".