AEG Landslides Virtual Symposium

Speaker Bios and Abstracts

Corey Froese, P.Eng., P.Geo

Wavelength Advisory Services

Corey Froese is a Principal Geological Engineer with Wavelength Advisory Services (Edmonton, Alberta) and has over 30 years of experience focused on risk management related to geological hazards with a focus on application of remote sensing applications. Corey leads teams in developing regional scale warning systems to integrate regional displacement trends data, cloud-based geospatial climatic data and data analytics to support the development of regional awareness and warning models. Corey's expertise has be recognized internationally through his roles as an expert advisor for the Norwegian government and Canadian Space Agency and as an Adjunct Professor at the University of Alberta.

Abstract:
Harnessing Satellite-Based Monitoring and Global Hydroclimatic Datasets to Support Operational Regional Landslide Forecasting

Over the past five years, parallel projects in Western Canada, Washington State and the Appalachian Plateau have utilized a combination of remote and in-situ data coupled with historical hydroclimatic data obtained from both re-analysis models and measurements. By developing a historical understanding as to the drivers for landslide activity, these drivers can be integrated into awareness and forecast models to support enhanced understanding of temporal and spatial patterns to support operational decisions. This presentation provides an overview of the key learnings and opportunities presented with the advent of new datasets.

Jillian Nicolazzo

Alaska Department of Natural Resources

Jillian Nicolazzo graduated from the University of Alaska, Fairbanks, with a B.S. in geological engineering and is currently the Acting Program Manager of the Landslide Hazards Program for the State of Alaska. Jillian works with partner agencies to map landslides across the state, assess and model landslide hazards, and communicate these hazards to communities. Prior to this, Jillian worked at the Department of Transportation & Public Facilities as an engineering assistant, where she contributed to bridge foundation design, and led the unstable slopes program, as part of the Asset Management team.

Abstract:
Comparing Pre- and Post-Event Lidar Data of the November 2023 Landslides on Wrangell Island, Alaska

Two large rain-induced landslides occurred late in the evening on November 20, 2023, on Wrangell Island, Alaska. The largest buried part of the Zimovia Highway, destroyed homes, resulted in six fatalities, and caused long-lasting impacts to the community. In this study, we compared pre- and post- event lidar datasets to quantify landslide volume and spatial extent. By differencing these high-resolution digital elevation models, we identified and mapped areas of erosion and deposition, which allowed us to also identify two previously unknown landslides in the area. These results highlight the importance of baseline lidar data, and the value of repeat lidar surveys for detecting landscape change and hazard assessment.

November 2023 Wrangell, Alaska, landslides.jpg

André Silva

Measurand

André Silva is a civil engineer with a master’s degree in Geotechnics from the Faculty of Engineering of the University of Porto (FEUP), Portugal. He has over 15 years of international experience across different countries specializing in geotechnical instrumentation and monitoring for mining, dams, tunnels, slopes and civil infrastructure.

He currently works at Orica Digital Solutions as part of the Technical Services / Support Management team at Measurand, where he provides global technical support, with a strong focus on automated monitoring systems, data quality, system integration, and field implementation, particularly in Latin America. André is President of the Technical Commission for Geotechnical Instrumentation and Monitoring (CT-IMG) of the Brazilian Geotechnical Society (ABMS) and an active member of the Portuguese Geotechnical Society (SPG). He also represents Brazil in the ISSMGE International Technical Committee TC220, dedicated to Geotechnical Instrumentation and Monitoring, contributing to international technical discussions, guidelines, and knowledge dissemination.

Abstract:
How to Select the Best Inclinometer Solution for Landslide Monitoring

Choosing the right inclinometer system is one of the most important steps in developing an effective landslide monitoring plan. With options such as Manual Inclinometer surveys, In-Place Inclinometers (IPI), and ShapeArray/Segmented Automated Arrays (SAA), each technology offers different advantages depending on the monitoring objectives, site conditions, expected displacement rates, and project constraints. This presentation will discuss the main factors that should guide the selection process and present a practical comparison of each solution, helping engineers and monitoring professionals understand which technology is best suited for different landslide applications. The session will provide a structured approach to selecting the most appropriate system to improve data quality, optimize monitoring strategy, and support better geotechnical decision-making.

Luke Weidner, Ph.D., P.E.

BGC Engineering

Luke Weidner is a Geological Engineer whose work centers on remote sensing applications in landslide, rockfall, and erosion research. He earned both his B.Sc. and Ph.D. in Geological Engineering and has been with BGC Engineering since 2021. He also holds a research associate position at the Colorado School of Mines. His work spans a range of frontiers in geohazard monitoring and analysis, from low-cost automated rock slope monitoring systems to 3D time-dependent hydraulic modeling of rock scour to advances in 3D change detection algorithms.

Abstract:
Studying Rockfall with Remote Sensing in 2026: What's New, What Works, and What It Changes

Rockfall monitoring has shifted from technology-limited to data-rich over the past decade, with lidar, photogrammetry, photomonitoring, and Doppler radar now making data collection routine. The bottleneck became analysis, but that is also changing. Advances in 3D displacement tracking, AI image interpretation, and machine learning point cloud classification now enable automated detection across large networks at low cost. This presentation reviews sensor selection principles, core monitoring objectives, and how these algorithmic developments are making continuous automated rockfall detection operationally feasible for any program that can fund data collection.

Jessie Hiatt

Colorado School of Mines

Jessie is a PhD candidate in Geological Engineering at Colorado School of Mines, where she also earned an M.S. in GIS and Geoinformatics and a B.S. in Geological Engineering. She is co-advised by Dr. Wendy Zhou and Dr. Lesli Wood. Her doctoral research focuses on integrating drone, satellite, and terrestrial-based datasets to characterize the surficial spatial and temporal dynamics of underground coal fires. Her work supports monitoring, hazard characterization and reclamation planning at legacy coal fire sites and abandoned mine lands across the western United States. She also works as a geologist and GIS analyst at Tetra Tech, supporting coal fire mitigation, mine reclamation, and geohazard assessment projects through field investigations and geospatial modeling.

Abstract:
UAS Photogrammetry Change Detection for Monitoring Coal Fire-Induced Surface Deformation

Unmanned Aerial Systems (UAS) provide a flexible and repeatable approach for collecting high-resolution remote sensing data in steep, unstable, or difficult-to-access terrain. This presentation highlights the use of UAS-based photogrammetry to evaluate coal fire-induced surface change and terrain instability in western Colorado. Optical UAS imagery collected between 2018 and 2025 was processed using photogrammetry to generate orthomosaics and point clouds. Orthomosaics were used to visually assess the development of fractures, subsidence features, landcover change and slope instability, while point-cloud comparison methods were used to quantify terrain deformation through time. This workflow links visual site interpretation with quantitative 3D change detection to document terrain evolution and support geohazard monitoring in complex and hazardous landscapes.

Corey Scheip, Ph.D., P.G. (NC)

BGC Engineering

Dr. Corey Scheip, P.G. (NC) is a geologist, geomorphologist, and geospatial data scientist based in the Appalachian Mountains of the eastern USA. He joined BGC in 2021 and works on understanding and characterizing landslides and landslide-prone landscapes. Dr. Scheip’s expertise is in geospatial data science, optical satellite imagery analysis, lidar analysis, and big data/cloud computing. He works closely with software and data science teams to integrate classical field-based Earth Science with modern workflows and computational methods with a focus on building scalable and transferrable solutions to classic geotechnical problems. 

Abstract:
Creating Rapid Landslide Inventories Across Large Areas with Deep Learning and Lidar Change Detection Data

Mapping landslides is an important but time-consuming task. Increased availability of multi-vintage lidar is affording first responders, practitioners, industry, and academia the ability to produce temporally constrained maps of ground surface change. Lidar change detection data can be produced at regular intervals through time or following a landslide triggering event. We have developed a deep learning approach to translate lidar change detection data, over areas of thousands of square kilometers, to preliminary landslide inventories. The workflow reduces manual effort by roughly 100x compared to human mappers and detects approximately 90% of landslides, often including features humans missed. We have also developed rapid fine-tuning workflows that enable adaptation of the base model to new geomorphic settings with as few as 600–700 additional training landslides, yielding substantial performance gains. We demonstrate results across diverse landslide morphologies, including small translational failures in Kentucky, long-runout debris flows in North Carolina, and low-angle glaciolacustrine landslides in the Western Canadian Sedimentary Basin, highlighting the model's potential for broad application.

Thomas Stanley

NASA, Goddard Space Flight Center

Thomas Stanley graduated from the University of Maryland in 2014 with a Masters in Geospatial Information Sciences. In the years since, he has collaborated with other scientists at Goddard Space Flight Center to develop static and dynamic models of landslide hazard at global and regional scales. These models rely on satellite-based estimates of precipitation, as well as remotely sensed observations of other variables.

Abstract:
Hindcasting Landslides after Hurricane Helene

In September 2024, a global landslide forecast showed the potential for widespread landsliding in the Southern Appalachians after the landfall of Hurricane Helene. As the disaster unfolded, it became clear that thousands of landslides occurred, primarily in western North Carolina. Although most landslides were reported in areas that had been predicted, the forecast also included large areas not known to be heavily impacted. Therefore, a review of the model began, including an assessment of precipitation data. For each candidate precipitation forecast, the distribution of rainfall was compared to ground-based datasets, and the hindcasted effects on landslide hazard assessment were evaluated. This experiment demonstrated that multiple high-resolution forecasts could have better captured the spatial distribution of landslides in this event.