Advances in Mine Pit Wall Geological Mapping using Unmanned Aerial Vehicle Technology and Deep Learning
Bibliographic record
Abstract
With rising costs and decreasing high-grade reserves, there has been an increased focus in mining operations to improve and to optimize current practices, including pit wall geological mapping. Proper mapping is critical for open pit mining operations since accurately and efficiently identifying the location, spatial variation, and type of geological features on mine faces will greatly decrease dilution and increase geological certainty. Conventional techniques rely on physically examining the pit walls in close proximity and laboratory testing of collected field samples, which are labour-intensive and can expose personnel to hazards such as falling rocks and machineries. Unmanned Aerial Vehicle (UAV) and deep learning (DL) techniques can improve and complement existing practices by efficiently acquiring high-resolution pit wall images and automatically predicting geological units. This thesis investigates the application of geological mapping using UAV-acquired RGB image data with DL models, and also demonstrates the advantages and limitations using these methods.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".