Prior knowledge-informed semantic segmentation framework for precise glacial lake mapping from multimodal imagery
Bibliographic record
Abstract
Variation in size and number of glacial lakes (GLs) is important indicators of climate change in the cryosphere and have attracted increasing research attention. However, publicly annotated datasets suitable for computer vision techniques, especially deep learning-based GL mapping, remain scarce. Moreover, existing datasets often contain noisy labels, which affect evaluation results and subsequently hinder downstream processes such as multimodal remote sensing data fusion. To address these issues, we propose a prior knowledge-informed framework for GL segmentation that integrates a self-training-based correction algorithm for glacial lake segmentation dataset (ST-CAGL), which iteratively refines noisy annotations without manual intervention. We also introduce a dual encoder glacial lake semantic segmentation network (DEGSNet) that has a cross-modal feature rectification module (CM-FRM) to enhance multimodal data fusion. Through comparative and ablation experiments, our method achieves an IoU of 86.26% and a DICE of 92.05% at the patch level, yielding improvements of 3.39% in IoU and 2.15% in DICE over the best-performing CNN-based model (UNet), and 5.92% in IoU and 4.33% in DICE over the best-performing Transformer-based model (SegFormer-B3), when these two models are trained with uncorrected labels. In addition, our framework demonstrates superior performance in extracting small GLs, compared to current works. The source code and dataset are available at https://github.com/tanhuizhi123/GlacierSeg . • A novel self-training based correction algorithm for glacial lake segmentation. • The dual encoder glacial lake segmentation network improves segmentation performance. • A large annotated multimodal glacial lake dataset benefits the community. • The framework improves recognition of small glacial lakes with low accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".