Mass-movement events across the cryosphere are expected to increase in frequency as air temperatures continue to warm, putting infrastructure at risk. The CryoSlideRisk project will bring together a convergent team of researchers, policy makers, and local community representatives through two workshops to address infrastructure resilience and adaptation to increasing mass-movement risks across the cryosphere.
We compare satellite images before the landslide event (Pre-Image) and after the landslide event (Post-Image). The impacted area of the landslide event was highlighted by a human labeler (Human Annotated Label). An AI model was able to identify the landslide event from the images (Prediction) with high confidence (Confidence Map). The developed AI model may be used to compile a landslide database in the regions of interest.
Predictive deep learning models can be created/trained to improve the prediction accuracy of where and when landslide hazards occur and the potentially-impacted areas based on the compiled database and other data sources, accounting for landslide contributing factors. This figure illustrates how AI models can be used to predict debris flows triggered by heavy rainfall from September 9-13, 2013 in Colorado's Front Range with high accuracy.
Project activities center around two workshops held in May 2022 and September 2023. We will use these workshops to bring together network members to identify gaps in our current understanding and develop methodologies to predict cryospheric mass-movement events.
Our specific goals are to:
May 12-13, 2022
Penn State
The Larson Transportation Institute (LTI) at Penn State hosted the first workshop in the spring of 2022.
The goals of this workshop were to:
September 7-8, 2023
University of Alaska Fairbanks
The University of Alaska Fairbanks hosted the second workshop in the fall of 2023.
The goals of this workshop were to:
