Receiving accurate and comprehensive knowledge about the conditions of roads after earthquake strike are crucial in finding optimal paths and coordinating rescue missions. Continuous coverage of the disaster region and rapid access of high-resolution satellite images make this technology as a useful and powerful resource for post-earthquake damage assessment and the evaluation process. Along with this improved technology, object-oriented classification has become a promising alternative for classifying high-resolution remote sensing imagery, such as QuickBird, Ikonos. Thus, in this study, a novel approach is proposed for the automatic detection and assessment of damaged roads in urban areas based on object based classification techniques using post-event satellite image and vector map. The most challenging phase of the proposed region-based algorithm is the segmentation procedure. The extracted regions are then classified using nearest neighbor classifier making use of textural parameters. Then, an appropriate fuzzy inference system (FIS) is proposed for road damage assessment. Finally, the roads are correctly labeled as 'Blocked road' or 'Unblocked road' in the road damage assessment step. The proposed method was tested on QuickBird pan-sharpened image of Bam, Iran, concerning the devastating earthquake that occurred in December 2003. The visual investigation of the obtained results demonstrates the efficiency of the proposed approach.