A generalized supervised classification scheme to produce provincial wetland inventory maps: an application of Google Earth Engine for big geo data processing
Why this work is in the frame
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Bibliographic record
Abstract
Wetlands are important natural resources due to their numerous ecological services. Consequently, identifying their locations and extents is imperative. The stability, repeatability, cost-effectiveness, multi-scale coverage, and proper spatial resolution imagery of satellites provide a valuable opportunity for their use in various large-scale applications, such as provincial wetland mapping. To do so, it is required to (1) process and classify big geo data (i.e. a large amount of satellite datasets) in a time- and computationally-efficient approach and (2) collect a large amount of field samples. In this study, Google Earth Engine (GEE) and machine learning algorithms were utilized to process thousands of remote sensing images and produce provincial wetland inventory maps of the three Canadian provinces of Manitoba, Quebec, and Newfoundland and Labrador (NL). Additionally, using GEE, a generalized supervised classification method is proposed to produce a regional wetland map from a large area (e.g., a province) when lacking field samples. In fact, using the field data from only Manitoba and assuming that all wetlands in Canada have similar characteristics, the wetland maps were generated for the other two provinces. The overall classification accuracies for Manitoba, Quebec, and NL were 84%, 78%, and 82%, respectively, indicating the high potential of the proposed method for aiding provincial wetland inventory systems.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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