Object-Oriented Machine Learning Strategies for Regional Scale Slope-Instability Identification From Earth Observation Data

Landslide processes are a major hazard concerning mountainous regions. Mapping unstable slopes is crucial for an effective hazard assessment, hence updated and reliable landslide catalogs are essential. The procedures for generating these catalogs are, however, mainly dependent on human-based interpretations. Depending on the extension of area of interest and available data, this procedure is very slow and usually biased. Fortunately, the constantly growing fleet of Earth Observation (EO) systems provides high-resolution information and repeat coverage required for mapping of unstable slopes. Automatic processing of this EO Big Data can reduce the dependency from human involvement, thereby increasing the frequency of updates to landslide catalogs. In this work, we study machine learning strategies for the analysis of slope instabilities at a regional scale. The idea is to reduce dependency from slow and subjective human-interpretation, which in turn will facilitate with the generation of quick, consistent and reliable landslide inventories. The available archive of EO datasets and landslide inventories will be used to train machine algorithms for automatic classification of stable and unstable slopes. We compare the effectiveness of individual and ensemble learners in object-based image analysis (OBIA) system. Deviating from the conventional practice of image segmentation from optical imagery, we introduce a new approach to defining objects by grouping the aspect values in digital elevation models (DEM). This pushes the definition of objects to resemble more like a hill-slope face and is a better representation for slope stability classification problem. We further identify key features which are critical in the determination of the object’s stability. A case study is presented for a region in the Himalayan range of North-Eastern Bhutan. A complete inventory of landslide was manually mapped by integrating the information from a 5-meters DEM, high-resolution optical images, and radar interferometry. This mapped inventory had a hold-out set for validation, while the rest was used for training the learning algorithms. Here we present the initial results and observation from this work. This work is done in the framework of European Commission's Horizon 2020 project "BETTER", with the main objective to facilitate the usage of large volume and heterogeneous datasets by downstream users, so that they can focus on the analysis of the extraction of the potential knowledge within the data and not on the processing of the data itself. More information is available on the website https://www.ec-better.eu/.