The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform
Why this work is in the frame
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Bibliographic record
Abstract
Development of the Canadian Wetland Inventory Map (CWIM) has thus far proceeded over two generations, reporting the extent and location of bog, fen, swamp, marsh, and water wetlands across the country with increasing accuracy. Each generation of this training inventory has improved the previous results by including additional reference wetland data and focusing on processing at the scale of ecozone, which represent ecologically distinct regions of Canada. The first and second generations attained relatively highly accurate results with an average approaching 86% though some overestimated wetland extents, particularly of the swamp class. The current research represents a third refinement of the inventory map. It was designed to improve the overall accuracy (OA) and reduce wetlands overestimation by modifying test and train data and integrating additional environmental and remote sensing datasets, including countrywide coverage of L-band ALOS PALSAR-2, SRTM, and Arctic digital elevation model, nighttime light, temperature, and precipitation data. Using a random forest classification within Google Earth Engine, the average OA obtained for the CWIM3 is 90.53%, an improvement of 4.77% over previous results. All ecozones experienced an OA increase of 2% or greater and individual ecozone OA results range between 94% at the highest to 84% at the lowest. Visual inspection of the classification products demonstrates a reduction of wetland area overestimation compared to previous inventory generations. In this study, several classification scenarios were defined to assess the effect of preprocessing and the benefits of incorporating multisource data for large-scale wetland mapping. In addition, the development of a confidence map helps visualize where current results are most and least reliable given the amount of wetland test and train data and the extent of recent landscape disturbance (e.g., fire). The resulting OAs and wetland areal extent reveal the importance of multisource data and adequate test and train data for wetland classification at a countrywide scale.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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