Assessment of Convolution Neural Networks for Wetland Mapping with Landsat in the Central Canadian Boreal Forest Region
Bibliographic record
Abstract
Methods for effective wetland monitoring are needed to understand how ecosystem services may be altered from past and present anthropogenic activities and recent climate change. The large extent of wetlands in many regions suggests remote sensing as an effective means for monitoring. Remote sensing approaches have shown good performance in local extent studies, but larger regional efforts have generally produced low accuracies for detailed classes. In this research we evaluate the potential of deep-learning Convolution Neural Networks (CNNs) for wetland classification using Landsat data to bog, fen, marsh, swamp, and water classes defined by the Canada Wetland Classification System (CWCS). The study area is the northern part of the forested region of Alberta where we had access to two reference data sources. We evaluated ResNet CNNs and developed a Multi-Size/Scale ResNet Ensemble (MSRE) approach that exhibited the best performance. For assessment, a spatial extension strategy was employed that separated regions for training and testing. Results were consistent between the two reference sources. The best overall accuracy for the CWCS classes was 62–68%. Compared to a pixel-based random forest implementation this was 5–7% higher depending on the accuracy measure considered. For a parameter-optimized spatial-based implementation this was 2–4% higher. For a reduced set of classes to water, wetland, and upland, overall accuracy was in the range of 86–87%. Assessment for sampling over the entire region instead of spatial extension improved the mean class accuracies (F1-score) by 9% for the CWCS classes and for the reduced three-class level by 6%. The overall accuracies were 69% and 90% for the CWCS and reduced classes respectively with region sampling. Results in this study show that detailed classification of wetland types with Landsat remains challenging, particularly for small wetlands. In addition, further investigation of deep-learning methods are needed to identify CNN configurations and sampling methods better suited to moderate spatial resolution imagery across a range of environments.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".