A Hybrid Approach Integrating 3D City Models, Remotely Sensed SAR Data and Interval-Valued Fuzzy Soft Set Based Decision Making for Post Disaster Mapping of Urban Areas

This chapter introduces a novel technique for post-disaster mapping and disaster scale estimation based on an integrated framework of SAR remote sensing and a 3D city model database, optical remote sensing imagery is used for validation purpose. SAR based urban damage detection is well established and has been used for many years. We have showed that how the existing 3D City Model can be updated with the information extracted from satellite remote sensing data. The third dimension will play a very crucial role in evacuation rout planning of damaged or affected buildings. In this study in the proposed–three-level (L₁, L₂, L₃)–model damage assessment information is integrated with the semantic knowledge of 3D city models in order to better organize the search and rescue operation. L₁ includes remotely sensed Synthetic Aperture Radar (SAR) space-borne data collection from the affected areas; L₂ includes a change detection process; and L₃ includes the estimation of the most affected building(s). Using this model, we show how the day-night image acquisition capability of a SAR sensor and semantic information from a 3D city model can be effectively used for post disaster mapping for rapid search and rescue operations. For L₁, the combination of very high resolution SAR data and a 3D city model in CityGML format is used. L₂ works under predefined conditions to detect the types of changes that have occurred. In L₃, an interval-valued fuzzy soft set theory method is proposed with which to estimate the scale of damage to the affected structures (buildings). In this study, we show the potential application of existing 3D city models (with semantic knowledge) in combination with SAR remote sensing for post disaster management activities, especially for search and rescue operations.