A New U-Net Based Convolutional Neural Network for Estimating Caribou Lichen Ground Cover from Field-Level RGB Images
Bibliographic record
Abstract
High-quality ground-truth data are critical for developing reliable Earth Observation (EO) based geospatial products. Conventional methods of collecting these data are either subject to an unknown amount of human error and bias or require extended time in the field to complete (i.e., point-intercept assessments). Digital photograph classification (DPC) may address these drawbacks. In this study, we first assess the performance of a DPC method developed through licensed software to estimate ground cover percentage (%) of bright lichens, a critical caribou forage in fall and winter when other food resources are scarce. We then evaluate the feasibility of replicating this workflow in an open-source environment with a modified U-net model to improve processing time and scalability. Our results indicate that DPC is appropriate for generating ground-truth data in support of large-scale EO-based lichen mapping within the boreal forests of eastern Canada. Our final open-sourced classification model, Lichen Convolutional Neural Network (LiCNN), is comparably accurate yet more efficient than the licensed workflow. Therefore, the LiCNN approach successfully addresses the mentioned shortcomings of conventional ground-truth data collection methods efficiently and without the need for specialized software.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".