Wetlands Mapping with Deep ResU-Net CNN and Open-Access Multisensor and Multitemporal Satellite Data in Alberta’s Parkland and Grassland Region
Bibliographic record
Abstract
Abstract Wetlands are a valuable ecosystem that provides various services to flora and fauna. This study developed and compared deep and shallow learning models for wetland classification across the climatically dynamic landscape of Alberta’s Parkland and Grassland Natural Region. This approach to wetland mapping entailed exploring multi-temporal (combination of spring/summer and fall months over four years – 2017 to 202) and multisensory (Sentinel 1 and 2 and Advanced Land Observing Satellite - ALOS) data as input in the predictive models. This input image consisted of S1 dual-polarization vertical-horizontal bands, S2 near-infrared and shortwave infrared bands and ALOS-derived Topographic Wetness Index. The study explored the ResU-Net deep learning (DL) model and two shallow learning models, namely random forest (RF) and support vector machine (SVM). We observed a significant increase in the average F1-score of the ResNet model prediction (0.77) compared to SVM and RF prediction of 0.65 and 0.64, respectively. The SVM and RF models showed a significant occurrence of mixed pixels, particularly marshes and swamps confused for upland classes (such as agricultural land). Overall, it was evident that the ResNet CNN predictions performed better than the SVM and RF models. The outcome of this study demonstrates the potential of the ResNet CNN model and exploiting open-access satellite imagery to generate credible products across large landscapes.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 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".