GeoBIA-based semi-automated landslide detection using UAS data: a case study of Uttarakhand Himalayas

Abstract

Recent developments in the field of Geographic Object-Based Image Analysis (GeoBIA) have been utilized for the Automation of Landslide detection. In this study, we have tried to develop a semi-automated detection methodology applying concepts of GeoBIA on High-Resolution Earth Observation imagery acquired from an Uncrewed Aerial Systems also referred to as Uncrewed Aerial Vehicles (UAV). The study area is in the Himalayan state of Uttarakhand, India. The UAV was flown over a landslide site. The UAV data was processed for deriving photogrammetric products (Digital Elevation Model and Orthomosaic). The methodology implements the segmentation and classification of UAV images using Multi-otsu thresholding method and machine learning algorithm of random forest. It incorporates spectral (RGB), textural (GLCM entropy and GLCM angular second moment), morphological (sky view factor), and topographical (elevation, slope, curvature) features derived from UAV photogrammetric products. When determining landslide locations is of utmost concern, this system's ability to detect landslides quickly and effectively gives it a viable alternative to manual procedures for landslide mapping across wide areas. The developed system was able to detect 86% of the total landslide area.