Classifying open water features using optical satellite imagery and an object-oriented convolutional neural network
Why this work is in the frame
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Bibliographic record
Abstract
In this study, Sentinel-2 optical satellite imagery was acquired over the Peace Athabasca Delta and assessed for its open water classification capabilities using an object-oriented deep learning algorithm . The workflow involved segmenting the satellite data into meaningful image objects, building a Convolutional Neural Network (CNN), training the CNN, and lastly applying the CNN, resulting in probability heat maps of open water (with score values ranging from 0–1). Using the vector segmentation, heat maps were then iteratively assigned final class labels (‘open water’ or ‘other’) based on various probability thresholding. The ensuing open water classifications were assessed against a large validation dataset, and a highest overall accuracy of 96.2% (0.912 kappa coefficient) was achieved, with an open water producer’s accuracy of 98.1%. These results were then compared against a Random Forest (RF) classification, and results indicated that the CNN algorithm outperforms RF in this study site. Additionally, an important component of this study was the optimization of several CNN configurations, including patch size and learning rate; the latter which plays a critical role in model adaptation. The optimized object-oriented CNN and associated results can be used to provide resource managers with accurate surface water extent maps at 10 m resolution.
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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