GLOFNet -- A Multimodal Dataset for GLOF Monitoring and Prediction
This provides a structured dataset for researchers in glacial hazard prediction, addressing data fragmentation, but it is incremental as it focuses on data collection rather than novel predictive methods.
The authors tackled the problem of predicting Glacial Lake Outburst Floods (GLOFs) by creating GLOFNet, a multimodal dataset integrating Sentinel-2 imagery, glacier velocity data, and temperature records, which revealed seasonal velocity cycles and long-term warming of ~0.8 K per decade.
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.