To address the growing challenges posed by geohazards and their cascading impacts, we invite contributions that advance reliable and scalable ML/AI approaches for the detection, monitoring, and prediction of geohazards (e.g., earthquakes, tsunamis, volcanoes, landslides, and surface subsidence). We especially welcome works that: (1) leverage multimodal and generative geospatial foundation models, including agentic and multi-agent approaches; (2) integrate multi-scale and multimodal sensing (e.g., optical/SAR, InSAR, distributed fiber-optic sensing) with ML/AI for pre- and post-event assessment of geohazards and their cascading impacts across diverse settings (urban, remote, post-disturbance, and cold-region environments); (3) develop interpretable and knowledge-guided ML/AI to reveal driving factors and physical mechanisms; (4) evaluate and improve model robustness in extreme and data-scarce scenarios through cross-region/cross-scenario transfer, uncertainty quantification, real-time data fusion, and parameter-efficient tuning; and (5) develop open-source, multi-scale, AI-ready geohazard datasets and benchmarks to support the pre-training, adaptation, and evaluation of next-generation geospatial foundation models for real-world deployment.

• EP - Earth and Planetary Surface Processes
• GC - Global Environmental Change
• H - Hydrology
• NS - Near Surface Geophysics