Wetland Mapping Using Multi-Spectral Satellite Imagery and Deep Convolutional Neural Networks: A Case Study in Newfoundland and Labrador, Canada
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Bibliographic record
Abstract
Due to the advent of powerful parallel processing tools, including modern Graphics Processing Units (GPU), new deep learning algorithms, such as Convolutional Neural Networks (CNNs), have significantly altered the state-of-the-art algorithms in satellite classification of complex environments. Recent studies have demonstrated that the generic feature maps extracted from CNNs are incredibly effective in wetland classification. The main drawback of very deep CNNs is described as structurally complex, causing the need for extensive training data. To address deep Convolutional Neural Network’s limitations, a timely and computationally efficient CNN architecture is proposed in this paper. The results of the proposed model were compared to other well-known CNNs (i.e., GoogleNet and SqueezeNet) and several machine learning algorithms, including Random Forest, Gaussian Naïve Bayes, and the Bayesian Optimized Tree. Results showed while significantly reduced the training time, the proposed deep learning method outperformed GoogleNet and SqueezeNet by about 12.71% and 12.2% in terms of mean overall accuracy, respectively. The classification results shown that the accuracy of wetland classes (fen, marsh, swamp, and shallow water) were significantly improved by applying the proposed CNN method.
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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 it