Landslide mapping from Sentinel-2 imagery through change detection
<|reference_start|>Landslide mapping from Sentinel-2 imagery through change detection: 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.<|reference_end|>
@article{monopoli2024landslide,
    title={Landslide mapping from Sentinel-2 imagery through change detection},
    author={Tommaso Monopoli, Fabio Montello, Claudio Rossi},
    journal={arXiv preprint arXiv:2405.20161},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20161},
    primaryClass={cs.CV eess.IV}
}