Performance Evaluation of Three Deep Learning Models for River ICE Segmentation From Aerial Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
River ice monitoring is of great importance due to its impact on various environmental issues. In this study, the performance of three deep learning models, U-Net, MA-FCN, and SegNet, in segmenting UAV images of river ice was compared using the Alberta river ice dataset. The models were evaluated using mean Intersection over Union (mIoU) and Pixel Accuracy (PA). Results showed U-Net achieved the best results with an mIoU of 65.5% and a PA of 88.45%, outperforming MA-FCN and SegNet. U-Net's lightweight design and skip connections preserved spatial information, making it more effective with limited training data. MA-FCN performed slightly weaker than U-Net and SegNet models due to it having more parameters, and SegNet's lack of skip connections led to smoother edges in the final segmentation map.
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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 | 0.001 | 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) | 0.001 | 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 it