Mapping landslides from EO data using deep-learning methods

Landslide hazard has always been a significant source of economic losses and fatalities in the mountainous regions. Knowledge of the spatial extent of the past and present landslide activity, compiled in the form of a landslide inventory map, is essential for effective risk management. High-resolution data acquired by Earth observation (EO) satellites are often used to map landslides by identifying morphological expressions that can be associated with past and/or recent deformation. This is a slow and difficult process as it requires extensive manual efforts. As a result, such maps are not readily available for all the landslide hazard affected regions. Fully automated methods are required to exploit the exponentially increasing amount of EO data available for landslide hazard assessments. In this context, conventional methods like pixel-based and object-based machine learning strategies have been studied extensively in the last decade. Recent advances in convolutional neural network (CNN), a type of deep-learning method, has outperformed other conventional learning methods in similar image interpretation tasks. In this work, we present a deep-learning based method for semantic segmentation of landslides from EO images. We present the results from a study area in the south of Portland in Oregon, USA. The landslide inventory for training and ground truth was extracted from the Statewide Landslide Information Database of Oregon (SLIDO). We were able to achieve a probability of detection (POD) greater than 0.70. This method can also be extended to be used for rapid mapping of landslides after a major triggering event (like earthquake or extreme metrological event) has occurred