About

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.

Join the network

Using AI for Landslide Detection and Forecasting

AI model

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.

Using AI models to predict debris flows triggered by heavy rainfall from September 9-13, 2013 in Colorado's Front Range with high accuracy

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.

Goals & Activities

Project activities center around two workshops that are planned to take place in the spring of 2022 and spring of 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:

develop more automated hazard mapping tools through remote sensing and machine learning
compile and make accessible relevant datasets and databases, and encourage interactions with potential users to enable the development and validation of AI algorithms for forecasting landslide risk
formulate more flexible and adaptive approaches to enhance infrastructure resiliency and adaptation

Workshops

May 12-13, 2022

Penn State
The Larson Transportation Institute (LTI) at Penn State will host the first workshop in the spring of 2022.

The goals of this workshop will be to:
1. facilitate networking among public agencies, engineering firms, stakeholders, and researchers;
2. synthesize the latest research developments in applying ML for mapping mass-movement hazards and applying AI for forecasting such hazards across the cryosphere;
3. identify concerns and challenges from a wide range of stakeholders and workshop participants on critical needs;
4. identify key knowledge gaps in reliable and accurate cryospheric hazard mapping and forecasting.
Workshop Details & Registration
Spring 2023

University of Alaska Fairbanks
The University of Alaska Fairbanks workshop in the spring of 2023 will build on the results of Workshop 1.

The goals of this workshop will be to:
1. facilitate networking among public agencies, engineering firms, stakeholders, and researchers;
2. identify public and private agency concerns and challenges related to mass-movement impacts on infrastructure in a changing climate;
3. develop research priorities to address identified challenges;
4. summarize challenges related to mass-movement impacts on infrastructure, and areas of future research, including AI and ML methods, which will be included in a peer-reviewed synthesis article.
(More information on the workshop can be found here as it is developed. Please check back again soon!)

Meet the PIs

- Margaret Darrow
- Professor
- Geological Engineering
- University of Alaska Fairbanks
- mmdarrow@alaska.edu
- Website
- Expertise
  
  Margaret Darrow's Expertise
  
  Margaret Darrow is a geological engineer and a registered Civil (Geotechnical) Engineer in the State of Alaska. Her research centers on frozen ground engineering, with projects investigating unfrozen water content in frozen soils, slope stability in permafrost, thermal modeling of infrastructure over permafrost, and the performance of geotechnical instrumentation in frozen ground.
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- Louise Farquharson
- Research Assistant Professor
- Geophysical Institute Permafrost Laboratory
- University of Alaska Fairbanks
- lmfarquharson@alaska.edu
- Google Scholar
- Expertise
  
  Louise Farquharson's Expertise
  
  Louise Farquharson is a geomorphologist and Quaternary geologist who focuses on Arctic landscape change. Louise is currently a Research Assistant Professor in the University of Alaska Fairbanks Geophysical Institute Permafrost Laboratory where she is involved in several NSF-funded projects that look at the interaction between permafrost thaw and its impact on both landscape dynamics and infrastructure.
  
  Louise is currently working in collaboration with the National Park Service to explore the influence of permafrost thaw on mass movements in Denali National Park.
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- Chaopeng Shen
- Associate Professor
- Civil and Environmental Engineering
- Pennsylvania State University
- cshen@engr.psu.edu
- Website
- Expertise
  
  Chaopeng Shen's Expertise
  
  Chaopeng Shen is associate professor in civil engineering at the Pennsylvania State University. He received his Ph.D. degree in environmental engineering from Michigan State University, East Lansing, MI, USA, in 2009. His Ph.D. research focused on computational hydrology and he developed the hydrologic model Process-based Adaptive Watershed Simulator (PAWS), which was later coupled to the community land model to study the interactions between hydrology and ecosystem.
  
  His recent efforts focused on harnessing the big data and machine learning opportunities in advancing hydrologic predictions and connecting physics with machine learning. He has written technical, editorial, review and collective opinion papers on hydrologic deep learning to call to attention the emerging opportunities for scientific advances. In addition, his research interests also include floodplain systems, scaling issues, process-based hydrologic modeling, and hydrologic data mining.
  
  He is currently an Associate Editor of the Water Resources Research and also an Associate Editor in Frontiers in AI.
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- Tong Qiu
- Professor
- Civil and Environmental Engineering
- Pennsylvania State University
- tuq1@psu.edu
- Website
- Expertise
  
  Tong Qiu's Expertise
  
  Tong Qiu is a geotechnical engineer and researcher who focuses on landslide hazards prediction and mitigation. Tong is currently a Professor in the Department of Civil and Environmental Engineering at the Pennsylvania State University – University Park.
  
  His past research focused on case history development and numerical analysis of landslide initiation and runout. Recently, he has been involved in a Google project in mapping and predicting rainfall-induced landslides at the regional/global scale using deep learning techniques. He is looking to extend these studies to permafrost-related landslides in the cryosphere.
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Contact us

cryosliderisk@alaska.edu