Explainable AI for Wetland Mapping Using High-Resolution Remote Sensing Data
Bibliographic record
Abstract
Wetlands are vital ecosystems for biodiversity. However, these regions encounter global degradation from human activities and climate change. This study introduces a convolutional neural network (CNN) model with a channel attention (CA) mechanism, called ACNN, for wetland mapping using high-resolution Planet satellite data. While achieving an Overall Accuracy of 0.96 on the training set and 0.90 on the validation set, our focus is on evaluating the transparency of the model's decision-making process. We employed the gradient-weighted class activation mapping (GradCAM) algorithm to quantify the robustness, faithfulness, localization, complexity, and randomization. Our results show a direct relationship between model depth and localization, indicating a refined focus on critical channels and features. However, a concurrent decrease in faithfulness, particularly in later layers, suggests a complexity-faithfulness trade-off. This study shows the significance of adopting Explainable Artificial Intelligence (XAI) methods to enhance the interpretability of deep learning (DL) models in wetland mapping.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".