Landslides are among the most serious threats to human lives and infrastructure in mountain ranges worldwide. Beyond the direct hazard through the moving mass, landslides can initiate natural hazard cascades by damming rivers and initiating catastrophic flash floods and debris flows. Through such long-range effects even unwitnessed landslides occurring in remote areas matter. Critically, insufficient information exists on landslide occurrence and recurrence intervals, and hence on the hazard potential of landslide hazard cascades, as well as possible prediction and prevention measures. This lack of information is mostly due to the remoteness of many mountain regions as well as the complex dynamics of natural hazard cascades even so the hazard posed by landslide dam failures is often orders of magnitudes greater than that of the initial landslide event. In the RiCoLa project, we developed, tested and applied remote sensing and process modeling approaches to detect existing and predict potential landslide dams and landslide-dammed lakes. The interdisciplinary RiCoLa project combined geomorphology, remote sensing, geoinformatics and hazard research in the frame of the Earth System Science Call of the Austrian Academy of Sciences (ÖAW) and facilitated the collaboration of researchers at the Department of Geography and Geology and the Interfaculty Department of Geoinformatics – Z_GIS at the University of Salzburg with colleagues from Taiwan and New Zealand. The central aim of the project was to detect landslide-induced river course changes and lake formation to analyze the role of predisposing (e.g., lithology), preparatory (e.g., climate) and triggering (e.g., earthquakes) factors in the formation of landslide-induced hazard cascades. For this, different tools for the detection and prediction of landslide dams and landslide-dammed lakes on satellite images and digital elevation models had to be developed, tested and applied to different mountain ranges worldwide.