River Ice Fine-Grained Segmentation: A GF-2 Satellite Image Dataset and Deep Learning Benchmark
Bibliographic record
Abstract
Semantic segmentation of river ice image serves as a critical technological foundation for hydrological monitoring and ice flood early warning system. Current publicly available river ice datasets predominantly utilize UAV-captured image and ground-based photographic observations. To address the limitations of spatial coverage in existing datasets, we present NWPU_YRCC_GFICE - a satellite remote sensing dataset constructed from multi-spectral GF-2 satellite images. The dataset innovatively categorizes river ice into six fine-grained classes across freeze-thaw cycles and covers river ice data from Yellow River (Ningxia-Inner Mongolia section) spanning the past 10 years. We further establish a comprehensive deep learning benchmark, which evaluates 33 state-of-the-art segmentation models and two improved segmentation models based on YOLO and Segformer architecture, separately. Experiments are conducted on the NWPU_YRCC_GFICE dataset and three public river ice datasets (NWPU_YRCC_EX, NWPU_YRCC2, and Alberta river ice segmentation dataset). The proposed models exhibit excellent performance, surpassing the state-of-the-art methods. The presented NWPU_YRCC_GFICE dataset and benchmark enriches the river ice dataset and favors in promoting fine-grained river ice segmentation research from satellite view. Our dataset and code is available at https://github.com/ASGOLabMultisourceCooperationGroup/NWPU_YRCC_GFICE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".