Landslides are recurrent natural hazards in mountainous regions of the Earth, claimingpeople’s lives and destroying man-made infrastructures. Society and policy makers areinterested in knowing the location and spatial extent of landslides immediately after atriggering event, as well as in predicting landslides. Detailed, accurate and completelandslide maps are essential to meet these interests. Promoted by the increased availabilityand quality of Earth Observation (EO) data, attempts to automate the production of landslidemaps have recently been undertaken. As demonstrated by several case studies, especiallyobject-based image analysis (OBIA) provides methodologies that are valuable for automatedlandslide mapping. However, existing OBIA mapping routines have been targeted to specificdata sets and study areas, and can therefore be hardly transferred to other data sets orgeographical settings. In order to achieve a high degree of automation, objectivity, andgeneral applicability of an object-based landslide mapping system, the proposed project aimsat the following developments:

• Automated knowledge-based and statistical OBIA routines that are optimised forapplication to VHR (very high resolution) and HR (high resolution) optical EO data setsfor the polygon-based extraction of landslides after a triggering event;
• OBIA mapping routines that are automated and at the same time robust and flexible tochanging input optical EO data sets (and thus spatial resolution), as well geographical setting;
• Automated knowledge-based and statistical OBIA routines that are optimised forapplication to multi-temporal VHR and HR optical EO data sets for the polygon-baseddetection of landslide changes;
• An innovative and efficient web processing chain which comprises robust OBIAmapping routines for feeding a pre-operational service that allows for automatedpolygon-based landslide mapping based on VHR and HR optical EO data, as well asthe identification of landslide-affected infrastructure.

For accomplishing the above-mentioned developments four to six study areas with differentenvironmental characteristics will be defined in Austria and South Tyrol, Italy. Various HRand VHR optical EO data sets will be acquired for these areas, including also very recentsatellite data such as Sentinel-2. A comprehensive analysis of different optical data fordistinct geographical areas is highly innovative and will lead to the development of a robustand reliable landslide mapping system using OBIA. This system is essential for theimplementation of a pre-operational service. It is expected that this service will be close tothe needs of potential users, and will provide users with extensive and reliable information onlandslides. All products of the proposed projects, i.e. maps, routines, and the service, will bethoroughly evaluated: the spatial and thematic accuracy of landslide maps will be quantified;routines will be evaluated by innovative robustness indices; the usability and relevance of theservice will be validated by potential users. A comprehensive validation report will documentmajor findings.