Classification System of Gis-objects Using Multi-sensorial Imagery for Near-realtime Disaster Management

In this paper, a near-realtime system for classification of GIS-objects is presented using multi-sensorial imagery. The system provides a framework for the integration of different kinds of imagery as well as any available data sources and spatial knowledge, which contributes information for the classification. The goal of the system is the assessment of infrastructure GIS-objects concerning their functionality. It enables the classification of infrastructure into different states as destroyed or intact after disasters such as floodings or earthquakes. The automatic approach generates an up-to-date map in order to support first aid in crisis scenarios. Probabilities are derived from the different input data using methods such as multispectral classification and fuzzy membership functions. The main core of the system is the combination of the probabilities to classify the individual GIS-object. The system can be run in a fully automatic or semi-automatic mode, where a human operator can edit intermediate results to ensure the required quality of the final results. In this paper, the performance of the system is demonstrated assessing road objects concerning their trafficability after flooding. By means of two test scenarios the efficiency and reliability of the system is shown. Concluding remarks are given at the end to point out further investigations.