Landslide mapping from Sentinel-2 imagery through change detection

TL;DR

This paper presents a novel deep learning approach for automatic landslide detection using Sentinel-2 satellite imagery and DEM data, along with a new validated landslide dataset, to improve change detection accuracy.

Contribution

It introduces a new deep learning architecture for bi-temporal change detection combining Sentinel-2 images and DEM data, and provides a validated open-access landslide dataset.

Findings

The proposed model outperforms existing change detection methods.

The new dataset enhances landslide detection research.

Open-source code facilitates further development.

Abstract

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.