Ultrahigh‐resolution mapping of peatland microform using ground‐based structure from motion with multiview stereo
Bibliographic record
Abstract
Abstract Microform is important in understanding wetland functions and processes. But collecting imagery of and mapping the physical structure of peatlands is often expensive and requires specialized equipment. We assessed the utility of coupling computer vision‐based structure from motion with multiview stereo photogrammetry (SfM‐MVS) and ground‐based photos to map peatland topography. The SfM‐MVS technique was tested on an alpine peatland in Banff National Park, Canada, and guidance was provided on minimizing errors. We found that coupling SfM‐MVS with ground‐based photos taken with a point and shoot camera is a viable and competitive technique for generating ultrahigh‐resolution elevations (i.e., <0.01 m, mean absolute error of 0.083 m). In evaluating 100+ viable SfM‐MVS data collection and processing scenarios, vegetation was found to considerably influence accuracy. Vegetation class, when accounted for, reduced absolute error by as much as 50%. The logistic flexibility of ground‐based SfM‐MVS paired with its high resolution, low error, and low cost makes it a research area worth developing as well as a useful addition to the wetland scientists' toolkit.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".