Wetland Classification with Deep ResU-Net Convolutional Neural Network and Multitemporal Sentinel-1 & 2 Imagery and ALOS Elevation Data: A Case Study in Alberta Parkland & Grassland Natural Region, Canada
Bibliographic record
Abstract
The study aimed to develop a Deep Learning (DL) model for a large-scale wetland classification in Alberta's Parkland and Grassland Natural Region (PGNR) using a fusion of multi-temporal Sentinel-2 (S2) optical and Sentinel-1 (S1) radar data and topographic data. A key objective of the study was to compare the performance of the ResNet model with two shallow learning techniques (namely Random Forest (RF) and Support Vector Machine (SVM)). A 25-band multi-seasonal (acquired over the summer/fall months of 2017 to 2020) image stack comprised of S1 (dual-polarization vertical-horizontal (VH) bands) and S2 (near-infrared (band 8) and shortwave infrared (band 11)) images and Advanced Land Observing Satellite (ALOS) derived Topographic Wetness Index as input data in the three models. Comparing the three products' accuracy metrics showed that the CNN model significantly outperformed the shallow machine learning models (SVM and RF). The best performing model was the ResU-Net model, with overall accuracy and overall kappa of 74% and 0.62, followed by SVM (69% and 0.55) and RF (0.68 and 0.54), respectively. The relative F1 scores of the mapped wetlands (marsh, open water, and swamp) using the shallow ML models showed deficiencies in their predictive capabilities. The average F1 score of the ResNet model was 0.77 compared to 0.65 (for SVM) and 0.64 (for RF). Compared to the ResNet CNN predictions, it was evident that this DL technique outperformed the shallow ML techniques evaluated in the study.
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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 | 0.001 | 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) | 0.000 | 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 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".