Boreal biome wetland classification using multi-seasonal EO data on gee and machine learning optimization/XAI modelling
Bibliographic record
Abstract
The objective of this study was to map wetlands representing 2017-2022 conditions in two areas in Canada’s Boreal Forest, specifically Nunavut’s taiga shield ecozone and Saskatchewan’s boreal shield ecozone. Wetland classification was performed by leveraging machine learning (ML) modelling on the Google Earth Engine (GEE) JavaScript API, and Python programming for model cross validation/optimization and explainbility. Robust wetland coverage estimates were derived by summarizing the predictions of several ML models (i.e., an ensemble) trained on different sample subsets. Moreover, this study employed two key components to improve the wetland mapping modelling: (1) the processing of multi-temporal, multiseasonal (summer and fall) satellite imagery, since timeseries observations capture shifting eco-hydrological conditions, and (2) the use of Grey Level Co-occurrence Matrix (GLCM) textural variables, which are understudied in the GEE literature. Models were interpreted using Shapley explainable AI (XAI) methods. Overall accuracies of > 95% suggest this is a promising methodology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".