neuro fuzzy inference system

Earthquake is one of the most catastrophic natural disasters to affect mankind. One of the critical problems after an earthquake is building damage assessment. The area، amount، rate، and type of the damage are essential information for rescue، humanitarian and reconstruction operations in the disaster area. On the other hand، to deal with the situation requires well organized and effective emergency planning. How quickly the event is responded and how efficiently response activities are managed are the main determinants of the overall cost of a disaster، both in terms of economic damage and fatalities. Remote sensing techniques play an important role in obtaining building damage information because of their non-contact، low cost، wide field of view، and fast response capacities. Now that more and diverse types of remote sensing data become available، various methods are designed and reported for building damage assessment. This paper provides a comprehensive review of these methods based on using optical images in three categories: mono، multi temporal and combination of images and vector map approach and also implements an automatic damage assessment method of buildings using high resolution satellite image and GIS layers. In this method، after extracting texture features of candidate buildings from both pre- and post event images and defining optimized features، a neuro fuzzy inference system was designed that determines buildings to four damage levels: Undamaged، Moderate damage، Heavy damage and Destroyed levels. Evaluation results show that designed system has the overall accuracy equal to 89% in classifying buildings to the four damage levels.