An attention-based multiscale few-shot network for water body extraction from GF-2 satellite imagery
Bibliographic record
Abstract
The core to water resources mapping is water body extraction, a pivotal technique employed to distinguish areas of water bodies from land and other regions. Presently, water body extraction methods relying on remote sensing images predominantly utilize supervised learning approaches. However, these methods are limited to fixed areas and demand a substantial amount of manually annotation data for the accurate identification of water bodies. This paper introduces a novel perspective by treating water body extraction as a few-shot semantic segmentation task, aiming for generalization across diverse geographic regions. This paper proposes a hierarchical atrous spatial pyramid pooling (ASPP) attention module to enhance the model’s extraction capabilities in multi-scale water bodies to highlight the characteristics of water bodies at different scales. Quantitative evaluation on test dataset shows that our method achieves average precision of 94.01%, average recall of 90.81%, average IoU of 90.14%, and average Fscore of 91.31%. Comparative analyses also prove that our method can extract water bodies across geographical areas, providing new ideas for water resources 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".