  Landslides are one of the most critical and destructive geohazards.
Widespread development of human activities and settlements combined with the
effects of climate change on weather are resulting in a high increase in the
frequency and destructive power of landslides, making them a major threat to
human life and the economy. In this paper, we explore methodologies to map
newly-occurred landslides using Sentinel-2 imagery automatically. All
approaches presented are framed as a bi-temporal change detection problem,
requiring only a pair of Sentinel-2 images, taken respectively before and after
a landslide-triggering event. Furthermore, we introduce a novel deep learning
architecture for fusing Sentinel-2 bi-temporal image pairs with Digital
Elevation Model (DEM) data, showcasing its promising performances w.r.t. other
change detection models in the literature. As a parallel task, we address
limitations in existing datasets by creating a novel geodatabase, which
includes manually validated open-access landslide inventories over
heterogeneous ecoregions of the world. We release both code and dataset with an
open-source license.
