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


Landslides constitute a major natural hazard in almost all mountainous regions of the world. 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.

Overall Objective

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.

Study areas

The methodology will be developed and applied in landslide affected study areas in Salzburg, Austria and in northern and southern Taiwan.


The project addresses the following main tasks, which are elaborated in seven work packages (WPs):

        The transformation of expert knowledge into digital signatures of landslide types
         The integration of multiple data sets from different sources and sensors
         The application of innovative and advanced OBIA methods
         The iterative validation of the procedures and results


The integrated semi-automated landslide delineation, classification and evaluation framework is designed to break new ground in the field of object-based landslide analysis, especially with respect to conceptual and methodological developments. The project will make an essential contribution towards the development of a methodology that is I) objective, II) transferable across areas, III) robust against changing input data and resolutions, and IV) automated.

Project duration

March 2013 - August 2015 (30 months)


Austrian Science Fund FWF: P 25446-N29
Project Volume: 230,422.50 EUR