Effective landslide risk reduction and response efforts require reliable detection, informed process understanding, and accurate prediction. Advances in data-driven landslide detection are accelerating post-event mapping and leading to a growing availability of multi-temporal landslide inventories. These datasets, in turn, are allowing researchers to obtain a deeper understanding of the causes and triggers that influence landslide activity from hillslope to regional scales. For example, in combination with hydroclimatic models, re-analysis products, and meteorological observations, such inventories are enabling improved quantification of dynamic hydro-meteorological conditions that trigger weather-related landslides. Similar efforts are revealing indicators of co-seismic landslide hazard and underlying causes of slope instability. These insights are being integrated into data-driven, predictive models that can inform hazard assessments, increase situational awareness, and aid warning.
This session aims to spur future research advances and operational application development by bringing together a wide range of perspectives from geomorphology, hydrology, meteorology, remote sensing, data science and beyond. We will additionally explore how artificial intelligence (AI) and other data-driven approaches can enhance traditional methodologies, offering new insights for landslide detection, process understanding, and prediction.
Topics may include:
• Detecting and mapping landslide activity with remote sensing data and/or point source terrestrial data
• Linking trends and variability in landslide activity to hydro-meteorological, geological, morphological, or other conditions to improve process understanding
• Development and testing of new methods and approaches, including statistical, machine learning, and AI-based approaches, to support landslide hazard assessment, prediction, and early warning
From detection to prediction: linking landslide causes, triggers, and outcomes
Co-organized by GM3/HS13
Convener:
Lisa LunaECSECS
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Co-conveners:
Sansar Raj MeenaECSECS,
Luca Piciullo,
Minu Treesa AbrahamECSECS,
Luca Ciabatta,
Oriol Monserrat,
Yaser Peiro