Development of a Historic Digital Elevation Model (hDEM) from Archival Aerial Imagery over the Black Mountain Alluvial Fan, Canada
Bibliographic record
Abstract
In a rapidly changing Arctic, reconstructing landscapes pre-warming is essential to understanding impacts due to climate-inducted geomorphic change. High-latitude elevation datasets extend temporally back to the 2000s, while region-wide warming became measurable in the 1980s. Historic aerial imagery archives provide datasets of high-resolution imagery from the mid- to late- 1900s with stereo-capability that can be harnessed to create historic digital elevation models, or hDEMs. A major issue with reconstructing a surface from the past is finding a way to constrain it in space, given a lack of ground control from that era, especially at high latitudes. The main purpose of this study was to determine if an hDEM could be used to detect altimetric change in an area of poor ground control. I developed an hDEM from historic aerial imagery over the Black Mountain alluvial fan complex in NT, Canada, and used satellite imagery-derived ground control points to constrain the model in space. The resulting hDEM qualitatively and quantitatively displayed geomorphic realism, with few interpolation artifacts along the model edges, over water surfaces, and in places of shadows. When compared with the ArcticDEM, the hDEM displayed a vertical RMSE of 5.19m. I was able to isolate approximately 30-40m of altimetric change from a landslide (c.2013-2016) in the Black Mountain Fan catchment, supporting the supervised use of hDEMs for change detection studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".