A knowledge-based method for road damage detection using high-resolution remote sensing image

Road damage detection from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pair of pre-disaster and post-disaster road data for change detection are difficult to obtain due to the mismatch of different data sources, especially for rural areas where the pre-disaster data (i.e. remote sensing imagery or vector map) are hard to obtain. In this study, a knowledge-based method for road damage detection solely from post-disaster high-resolution remote sensing image is proposed. The road centerline is firstly extracted based on the preset road seed points. Then, features such as road brightness, standard deviation, rectangularity, aspect ratio are selected form a knowledge model. Finally, under the guidance of the road centerline, the post-disaster roads are extracted and the damaged roads were detected by applying the knowledge model. The newly developed method is evaluated using a WorldView-1 image over Wenchuan, China acquired three days after the earthquake in May 15, 2008. The results show that the producer's accuracy (PA) and user's accuracy (UA) reached about 90% and 85% respectively, indicating that the proposed method is effective for road damage detection. This approach also significantly reduces the need for pre-disaster remote sensing data.