Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image

Remote Sensing

Jianhua Wang³  Qiming Qin³  Jianghua Zhao²  Xin Ye³  Xiao Feng³  Xuebin Qin³  Xiucheng Yang³  Gonzalo Pajares Martinsanz¹ 

[1] Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China;;Scientific Data Center, Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; E-Mails:;Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China; E-Mails:

关键词: high-resolution remote sensing image;    road centerline;    knowledge model;    damage detection;    assessment indicator;   

DOI  :  10.3390/rs70404948

来源: mdpi

【 摘 要 】

Road damage detection and assessment from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pairing of pre-disaster and post-disaster road data for change detection and assessment is difficult to achieve 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 and assessment 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, and aspect ratio are selected to form a knowledge model. Finally, under the guidance of the road centerline, the post-disaster roads are extracted and the damaged roads are detected by applying the knowledge model. In order to quantitatively assess the damage degree, damage assessment indicators with their corresponding standard of damage grade are also proposed. The newly developed method is evaluated using a WorldView-1 image over Wenchuan, China acquired three days after the earthquake on 15 May 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 and assessment. This approach also significantly reduces the need for pre-disaster remote sensing data.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.