AGU25 – Toward Reliable and Scalable Geohazard Intelligence

December 15–19, 2025 AGU25 – Toward Reliable and Scalable Geohazard Intelligence: From Multiscale Sensing to Open Data Foundations

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To address the growing challenges of geohazards, we invite contributions that advance reliable and scalable machine learning/artificial intelligence (ML/AI) approaches for the detection, monitoring, and prediction of geohazards, such as earthquakes, tsunamis, volcanoes, landslides, and surface subsidence. We especially welcome works that:

1. Integrate multi-scale sensing technologies (e.g., remote sensing and distributed fiber-optic sensing) with ML/AI to support both pre- and post-event assessment of geohazards as well as their cascading impacts in diverse environmental settings, such as urbanized, remote, post-disturbance, and cold landscapes;
2. Develop interpretable and knowledge-guided (e.g., physics-informed) ML/AI to reveal the driving factors and physical mechanisms;
3. Evaluate and improve model robustness in extreme and data-scarce scenarios through cross-region and cross-scenario model transfer, uncertainty quantification, and real-time data fusion;
4. Develop open-source, multi-scale geohazard datasets to support the training and testing of foundation models toward reliable, scalable, real-world AI deployment.

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Invited Speakers

• Benjamin Mirus, United States Geological Survey
• Ettore Biondi, Stanford University
• Joseph Wartman, University of Washington

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Session Details

NH33B – Toward Reliable and Scalable Geohazard Intelligence: From Multiscale Sensing to Open Data Foundations II (Oral) Wednesday, 17 December 2025 14:15 – 15:45 Room 297 (New Orleans Convention Center) Session Link

NH11D – Toward Reliable and Scalable Geohazard Intelligence: From Multiscale Sensing to Open Data Foundations I (Poster) Monday, 15 December 2025 08:30 – 12:00 Hall EFG (Poster Hall), New Orleans Convention Center Session Link

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