构建多源数据集GLOFNet，助力冰湖溃决洪水预测
GLOFNet -- A Multimodal Dataset for GLOF Monitoring and Prediction
- 融合卫星影像、冰川运动与地表温度三类数据
- 揭示冰川速度季节周期与近十年升温约0.8K/ decade
- 专为罕见灾害预测设计，适合气候与遥感研究者
冰湖溃决洪水（GLOFs）是高山地区罕见但破坏性强的灾害，但以往研究受限于零散且单一模态的数据。多数工作聚焦灾后测绘，而预测需整合视觉指标与物理前兆的协同数据。本文提出面向喀喇昆仑山希斯珀冰川的GLOFNet多模态数据集，融合哨兵-2多光谱影像、NASA ITS_LIVE冰川速度数据及超过二十年的MODIS地表温度记录。数据预处理包括云掩膜、质量过滤、归一化、时间插值、增强与周期编码，并实现多模态对齐。探索性分析显示冰川速度存在季节循环、近十年地表温度上升约0.8 K/十年，且冰冻圈条件空间异质性显著。GLOFNet公开可用，可支持未来冰川灾害预测研究。其针对类别不平衡、云污染与分辨率粗糙等挑战，为多模态深度学习在稀有灾害预测中的基准测试提供结构化基础。
原文摘要 · Abstract (English)
Glacial Lake Outburst Floods (GLOFs) are rare but destructive hazards in high mountain regions, yet predictive research is hindered by fragmented and unimodal data. Most prior efforts emphasize post-event mapping, whereas forecasting requires harmonized datasets that combine visual indicators with physical precursors. We present GLOFNet, a multimodal dataset for GLOF monitoring and prediction, focused on the Shisper Glacier in the Karakoram. It integrates three complementary sources: Sentinel-2 multispectral imagery for spatial monitoring, NASA ITS_LIVE velocity products for glacier kinematics, and MODIS Land Surface Temperature records spanning over two decades. Preprocessing included cloud masking, quality filtering, normalization, temporal interpolation, augmentation, and cyclical encoding, followed by harmonization across modalities. Exploratory analysis reveals seasonal glacier velocity cycles, long-term warming of ~0.8 K per decade, and spatial heterogeneity in cryospheric conditions. The resulting dataset, GLOFNet, is publicly available to support future research in glacial hazard prediction. By addressing challenges such as class imbalance, cloud contamination, and coarse resolution, GLOFNet provides a structured foundation for benchmarking multimodal deep learning approaches to rare hazard prediction.
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