Landslides and Climate Change in highly Sensible Environments: Seismology, Earth Observation and Artificial Intelligence – HighLand
The HighLand project proposes to combine seismology, remote sensing and machine learning to quantify the impact of climate on mass-wasting activity in regions of high latitude or altitude. The first objective of the project is the development of new processing chains to build, from the continuous recordings produced by regional seismological networks, instrumental catalogs of landslides. The systematic exploration of these seismological chronicles will be made possible by the use of machine learning algorithms and will enable the production of catalogs offering unparalleled spatio-temporal resolution. The seismological detection will be confronted with satellite observations with high temporal repetition possible thanks to the constellations of Sentinel and Landsat satellites. Three regions of the world will first be targeted by this new processing chain: Alaska, the Alps and Nepal. This multi-disciplinary approach will make it possible to produce the necessary observations and to build and constrain models to better understand the long and short-term links between climate and mass wasting activity. The prototype of the processing chain will serve as the basis for a system for observing and listening to the landside activity in near real time in these regions of the world and then on a global scale.
Monsieur Clément Hibert (Institut de Physique du Globe de Strasbourg (UMR 7516))
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
IPGS Institut de Physique du Globe de Strasbourg (UMR 7516)
Help of the ANR 337,100 euros
Beginning and duration of the scientific project: December 2020 - 42 Months