WetMapFormer: A unified deep CNN and vision transformer for complex wetland mapping
Bibliographic record
Abstract
The Ramsar Convention of 1971 encourages wetland preservation, but it is unclear how climate change will affect wetland extent and related biodiversity. Due to the use of the self-attention mechanism, vision transformers (ViTs) gain better modeling of global contextual information and become a powerful alternative to Convolutional Neural Networks (CNNs). However, ViTs require enormous training datasets to activate their image classification power, and gathering training samples for remote sensing applications is typically costly. As such, in this study, we develop a deep learning algorithm called (WetMapFormer), which effectively utilizes both CNNs and vision transformer architectures for precise mapping of wetlands in three pilot sites around the Albert county, York county, and Grand Bay-Westfield located in New Brunswick, Canada. The WetMapFormer utilizes local window attention (LWA) rather than the conventional self-attention mechanism for improving the capability of feature generalization in a local area by substantially reducing the computational cost of vanilla ViTs. We extensively evaluated the robustness of the proposed WetMapFormer with Sentinel-1 and Sentinel-2 satellite data and compared it with the various CNNs and vision transformer models which include ViT, Swin Transformer, HybridSN, CoAtNet, a multimodel network, and ResNet, respectively. The proposed WetMapFormer achieves F-1 scores of 0.94, 0.94, 0.96, 0.97, 0.97, 0.97, and 1 for the recognition of aquatic bed, freshwater marsh, shrub wetland, bog, fen, forested wetland, and water, respectively. As compared to other vision transformers, the WetMapFormer limits receptive fields while adjusting translational invariance and equivariance characteristics. The codes will be made available publicly at https://github.com/aj1365/WetMapFormer.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".