Combining Remote Sensing Techniques to Optimize Digital Surface Models for Change Detection – A Case Study at a Pit Wall in the Canadian Cordillera
Bibliographic record
Abstract
Slope monitoring is vital for open pit mines during operations and nearing closure where pit wall instabilities are present. Change detection using terrestrial laser scanning (TLS) has become part of the state of practice for collecting accurate measurements with extensive spatial coverage. The approach can detect displacements associated with global slope movement, as well as surficial occurrences such as rockfalls and sloughs. However, TLS acquisition quality is vulnerable to atmospheric interference and reflectivity of the target materials. In this regard, the high-density surface information obtained from TLS could be complemented with other, less detailed, remote sensing information in order to construct a complete surface model. Such an approach, however, requires a systematic workflow and a means to calculate surface model reliability. The subject of this study is an unstable pit wall at an open pit coal mine transitioning to closure. The site is located in the Canadian Cordillera, in a setting with intermittent fog and cloud cover, wildfire haze, and seepage within the slope face and toe. Insufficient TLS data can be obtained in local areas of the slope impacted by these conditions, as well as in shadowed areas on the benches above the TLS base elevation. Aerial photogrammetry was undertaken using UAV and combined with the TLS scan to generate a representative surface model. This paper presents a methodology for registration improvement between the TLS and UAV photogrammetry clouds, and evaluation of the combined surface using concepts of Limit of Detection (LOD) adopted from change detection techniques.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".