Due to the progressive development of urban areas and infrastructure, more and more people settle in environments that are or become endangered by mass movements. This situation is being complicated by the fact that the dependency of our society on a functioning infrastructure and number of human or objects in endangered areas increases at the same time. Early warning and alarm systems are an efficient tool to face landslide hazard and reduce the risk landslides, especially where no other mitigation strategies are suitable. Currently existing monitoring systems for early warning are available in terms of monolithic systems. This is a very cost-intensive way considering installation as well as operational and personal expenses. This displays the demand for modern cost-efficient technologies to upgrade existing and to develop new systems.
The main purpose of the SLEWS project is the development of a prototypic real-time landslide monitoring and early warning system for an efficient landslide hazard management. In this context especially the enhancement of data quality to improve recognition and analysis of hazardous situations and reduction of false alarm rates are of special interest. The whole information chain from data gathering, validation and interpretation to data retrieval, visualization and user specific warning is subject of the research project. The project focuses on innovative web technologies using standards according to the Open Geospatial Consortium (OGC) sensor web enablement (OGC SWE) and cost efficient but reliable micro sensors (MEMS) from the automotive industry. The integration of heterogeneous data and information from various providers may be established by an open platform strategy using the web service technology. The Spatial Data Infrastructure (SDI) integrates modern sensor technologies, data gathering, storage and retrieval as well as services for data validation, processing and alarm generation.
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