The Mountain Habitats Segmentation and Change Detection Dataset
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we present a challenging dataset for the purpose of segmentation and change detection in photographic images of mountain habitats. We also propose a baseline algorithm for habitats segmentation to allow for performance comparison. The dataset consists of high resolution image pairs of historic and repeat photographs of mountain habitats acquired in the Canadian Rocky Mountains for ecological surveys. With a time lapse of 70 to 100 years between the acquisition of historic and repeat images, these photographs contain critical information about ecological change in the Rockies. The challenging aspects of analyzing these image pairs come mostly from the perspective (oblique) view of the photographs and the lack of color information in the historic photographs. The baseline algorithm that we propose here is based on texture analysis and machine learning techniques. Classifier training and results validation are made possible by the availability of expert manual ground-truth segmentation for each image. The results obtained with the baseline algorithm are promising and serve as a reference for new and improved segmentation and change detection algorithms.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it