ASGOLabMultisourceCooperationGroup/NWPU_YRCC_GFICE

NWPU YRCC GFICE

NWPU_YRCC_GFICE (GFICE) is a satellite remote-sensing semantic segmentation dataset for fine-grained river-ice mapping. It is built from multi-spectral GF-2 satellite imagery over the Yellow River (Ningxia–Inner Mongolia section) and is designed to support hydrological monitoring and ice-flood early warning research.

Dataset Details

Dataset Description

The NWPU_YRCC_GFICE dataset provides pixel-level annotations for river-ice scenes captured from satellite view, addressing the limited spatial coverage of prior river-ice datasets that mainly rely on UAV or ground-based imagery. The dataset covers freeze–thaw cycles and annotates river-ice conditions into eight fine-grained semantic classes.

• Curated by: Northwestern Polytechnical University (NWPU) research team (authors of the companion paper)
• License: CC BY-NC-SA 4.0 (cc-by-nc-sa-4.0)

Uses

Direct Use

• Training and evaluating semantic segmentation models for river ice mapping from satellite imagery.
• Research on fine-grained ice characterization across freeze–thaw cycles.
• Developing algorithms for hydrological monitoring and ice-flood early warning (research/non-commercial use under the dataset license).
• Benchmarking segmentation architectures (the companion work evaluates many segmentation models and includes improved YOLO-/SegFormer-based variants).

Out-of-Scope Use

• Commercial use without meeting the constraints of CC BY-NC-SA 4.0.
• Direct deployment to other rivers/regions/sensors without considering domain shift (different illumination, sediment, width, climate, sensor bands, spatial resolution, etc.).

Dataset Structure

The dataset is intended for semantic segmentation with mmsegmentation dataset style. A typical sample contains:

• image: a multi-spectral GF-2 TIFF image
• label: a per-pixel label map with 8 classes (integer IDs)

Splits (recommended):

• train set contains 22700 images
• validation set contains 5675 images

Label schema: The dataset contains eight fine-grained classes for river ice across freeze–thaw cycles.

class_id	class_name
0	land
1	water
2	shore_ice
3	other_ice
4	snow_covered_ice
5	stream_ice
6	ice_in_river
7	ice_outside_river

Citation

BibTeX:

@ARTICLE{11145880,
  author={Wei, Chenxu and Li, Haoxuan and Chen, Liang and Zhou, Haohao and Taukebayev, Omirzhan and Wu, Wencong and Temirbayev, Amirkhan and Han, Lin and Ran, Lingyan and Yin, Hanlin and Wang, Peng and Liu, Junrui and Zhang, Xiuwei and Zhang, Yanning},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={River Ice Fine-Grained Segmentation: A GF-2 Satellite Image Dataset and Deep Learning Benchmark}, 
  year={2025},
  volume={63},
  number={},
  pages={1-15},
  keywords={Ice;Rivers;Satellites;Monitoring;Feature extraction;Transformers;Semantic segmentation;Accuracy;Benchmark testing;Remote sensing;Fine-grained semantic segmentation;river ice dataset;SegFormer;YOLO},
  doi={10.1109/TGRS.2025.3604644}
}

Addition Note: We found that a few images in the dataset associated with the paper contained processing errors, and we have corrected them in the currently released public version of the dataset.