Airborne and spaceborne remote sensing play a pivotal role in driving the innovation in detecting, monitoring and assessing landslide hazards. Automated and semi-automated approaches using optical images have already been developed to create dynamic multi-temporal inventories and to supplement field surveys. However, methods that use remotely sensed optical data cannot reliably support near real-time hazard assessments and early warning systems (EWS), since clear sky images may not be readily available prior to and during a given landslide event.

In light of the aforementioned challenges, synthetic aperture radar (SAR) offers new opportunities to support systematic mapping and monitoring of landslides over extensive regions and for the development of regional-scale landslide warning systems. With synoptic imaging capabilities, under inclement weather conditions and independent of sunlight conditions, SAR techniques provide invaluable information on landslide locations, boundaries, soil moisture content, and changes to vegetation within landslide bodies, based on the exploitation of radar amplitude and phase information.

The main goal of this project is to develop a novel hybrid multi-scale data fusion approach that incorporates information from all satellite remote sensing sources including optical images and radar data for near real-time change detection and assessing landslide stability and early warning indicators. A deep-learning based multi-sensor data fusion is developed to integrate data from various temporal and spatial scales for improved and more robust change detection of the effects caused by landslides