Integrated Semi-automated Landslide Delineation, Classification and Evaluation (iSLIDE)

Landslides constitute a major natural hazard in hilly and mountainous regions of the world. They claim the lives of many people each year, cause damage to all types of man-made structures and influence infrastructures from the local scale to the regional and even national scale.

Today, the wide range of available Earth Observation (EO) data implies the need for reliable and efficient methods for detecting, analysing and monitoring landslides in order to assist hazard and risk analysis. Object-based image analysis (OBIA) provides a great potential for semi-automated landslide detection and classification, since - in comparison to pixel-based approaches - not only spectral, but also spatial, morphometric, textural and contextual properties can be addressed. Through the integration of multiple data sets, landslides can be examined in a more efficient way, making use of the most suitable properties of the available information layers.

'The main objective of iSLIDE is to develop a methodological framework for landslide delineation, classification and evaluation through the integration of optical remote sensing data, digital elevation information and terrain unit layers using innovative OBIA methods. Additionally, the potential of Synthetic Aperture Radar (SAR) data for object-based landslide mapping is investigated.'