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Sabine Loos
University of Michigan Ann ArborMeeting roles in:
Building a Community-Curated, Open-Access Platform of Global Landslide Datasets to Support Reliable and Scalable AI Models
Advancing Post-Earthquake Damage Detection: Ground Validation and Deep Learning Approach from the 2023 Mw6.8 Morocco Earthquake
Evaluating Machine Learning Models for Predicting Post-Disaster Reconstruction Using InSAR: A Case Study of the 2015 Nepal Earthquake
Unveiling the Underappreciated Consequences of Landslides across the United States with Generative AI
Foundational geospatial databases and long-term monitoring to support the next generation of data-driven landslide hazard and risk assessments (Invited Paper 1855640)
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