Exploring Landscape Change in Mountain Environments With the Mountain Legacy Online Image Analysis Toolkit
Bibliographic record
Abstract
Since 1996, Mountain Legacy Project (MLP) researchers have been exploring change in Canada's mountain environments through the use of systematic repeat photography. With access to upwards of 120,000 systematic glass plate negatives from Canada's mountain west, the MLP field teams seek to stand where historic surveyors stood and accurately reshoot these images. The resulting image pairs are analyzed, catalogued, and made available for further research into landscape changes. In this article we suggest that repeat photography would fit well within the Future Earth research agenda. We go on to introduce the Image Analysis Toolkit (IAT), which provides interactive comparative image visualization and analytics for a wide variety of ecological, geological, fluvial, and human phenomena. The toolkit is based on insights from recent research on repeat photography and features the following: user-controlled ability to compare, overlay, classify, scale, fade, draw, and annotate images; production of comparative statistics on user-defined categories (eg percentage of ice cover change in each image pair); and different ways to visualize change in the image pairs. The examples presented here utilize MLP image pairs, but the toolkit is designed to be used by anyone with their own comparative images as well as those in the MLP collection. All images and software are under Creative Commons copyright and are open access for noncommercial use via the Mountain Legacy Explorer website. The IAT is at the beginning of its software life cycle and will continue to develop features required by those who use repeat photography to discover change in mountain environments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".