Abstract ID: 3.13369 | Accepted as Talk | Talk/Oral | TBA | TBA

Landslides pose a global threat, causing numerous fatalities and extensive damage annually. Climate change, population growth and infrastructure development are expected to exacerbate these risks, particularly in alpine regions. To cope with this changing risk, we urgently need improved monitoring and early warning technologies. Here we describe a new approach that leverages recent advancements in environmental seismology, mobile robotics, and artificial intelligence (AI) to monitor landslides at unprecedented spatial and temporal resolution. We aim to overcome the main limits to current technologies, which often require direct line of sight or event arrival at monitoring locations, providing either high temporal or spatial resolution, but rarely both. To do so, our proposed system utilizes environmental seismology for improved event detection and mobile robotics sensors with AI algorithms for high temporal and spatial resolution measurements of in-situ flow parameters. We plan to collaborate with cantonal authorities and relevant industry partners to access field sites and incorporate feedback for system optimization. Once validated and tested, this system will fill a critical gap in landslide characterization, and help to cope with changing risk in the future.