Structure from motion used to revive archived aerial photographs for geomorphological analysis: an example from Mount Meager volcano, British Columbia, Canada
Bibliographic record
Abstract
High-resolution topographic modeling has become more accessible due to the development of structure from motion (SfM)-image-matching algorithms in digital photogrammetry. Large archival databases of historical aerial photographs are available in university, public, and government libraries, commonly as paper copies. The photographs can be in poor condition (i.e., deformed by humidity, scratched, or annotated). In addition, the negatives, as well as metadata, may be missing. Processing such photographs using classic stereo-photogrammetry is difficult and, in many instances, impossible. SfM can be applied to these photosets to access the valuable archive of geomorphic changes over the past century. In this paper, we illustrate the utility of the SfM technique using 568 digitized vertical aerial photographs of Mount Meager volcano, located in southwestern British Columbia, Canada. We use the aerial photographs, which span the period from 1947 to 2006, to track glaciers and glacier–landslide interactions on the volcano. Over this period, glaciers have thinned and retreated, interrupted by minor advances in the 1960s and 1970s. Landslides are frequent on the volcano and contribute to debris cover on the glaciers affecting the ablation process. SfM processing of the aerial photographs allowed us to unlock geomorphic information and reconstruct landscape change that would otherwise have been impossible. The results from SfM provide a visually effective way of presenting landscape change to a broad public audience, as a form of virtual geoheritage. The approach can thus be broadly applied in scientific and professional practices for improving land planning and hazard management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".