MAPPING AND MONITORING GROUND INSTABILITIES WITH SENTINEL-1 DATA: THE EXPERIENCE OF SERNAGEOMIN

This paper presents a new framework for landslide detection using radar and deep learning, informed by data from 73000 landslides across diverse regions in the world. The method showed high accuracy and rapid response potential regardless of weather and illumination conditions. By overcoming the limits of optical satellite imagery, it offers a powerful tool for global landslide detection, benefiting disaster management and advancing methods for monitoring hazardous terrains.

Landslides and ground deformation associated with the construction of a hydropower mega dam in the Santa Cruz River in Argentine Patagonia have been monitored using radar and optical satellite data, together with the analysis of technical reports. This allowed us to assess the integrity of the construction, providing a new and independent dataset. We have been able to identify ground deformation trends that put the construction works at risk.