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.

翻译：滑坡是最关键且最具破坏性的地质灾害之一。人类活动与聚居地的广泛发展，加之气候变化对天气的影响，正导致滑坡发生频率与破坏力急剧上升，使其成为对人类生命与经济的主要威胁。本文探讨了利用Sentinel-2影像自动绘制新发滑坡图的方法。所提出的所有方法均被构建为双时相变化检测问题，仅需一对分别采集于滑坡诱发事件前后的Sentinel-2影像。此外，我们提出了一种新颖的深度学习架构，用于融合Sentinel-2双时相影像对与数字高程模型（DEM）数据，并展示了其相较于文献中其他变化检测模型的优异性能。作为并行任务，我们通过创建新型地理数据库解决了现有数据集的局限性，该数据库包含全球不同生态区经人工验证的开放访问滑坡清单。我们以开源许可证发布了代码与数据集。