Detecting damaged building parts in earthquake-damaged areas using differential seeded region growing. Issue 4 (16th February 2017)

• Record Type: Journal Article
• Authors: Yeom, Junho; Jung, Minyoung; Kim, Yongil
• Is Part Of: International journal of remote sensing. Volume 38:Issue 4(2017)
• Page Start: 985
• Page End: 1005
• Publication Date: 2017-02-16
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1274445
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Acquiring information about earthquake-damaged buildings is essential for effective rescue and restoration operations. Building damage must be assessed to provide detailed information regarding the location and proportion of damage to individual buildings. Automatic processing of damage assessment is also critical in hastening relief efforts. Therefore, we propose a new method for automatically extracting damaged building parts and quantitatively assessing the damage to individual buildings caused by earthquakes. The proposed method consists of four parts: generating differential information, differential seeded region growing (DSRG), rule-based earthquake damage analysis, and accuracy assessment. First, differential information is automatically derived to extract the damage candidates. The damage candidates are then used as seed points for the region growing process to extract damaged building parts without requiring intervention by a human analyst. Then, designed automated extraction rules based on the condition of the collapsed or crushed buildings are used on the DSRG results. We applied the proposed method to both a residential area and a business area in Port-au-Prince, Haiti, and evaluated its accuracy using a visual comparison, a location-based assessment, and a proportion-based assessment. The results of the visual comparison were similar to the reference data, exhibiting location accuracies of 86% and 89% for the chosen residential and business areas, respectively. An assessment of the damage proportion to individual buildings was performed, which showed that the proposed method achieved accuracies of 81% and 84% for the residential and business areas, respectively, and was highly correlated with the reference data. The proposed method can accurately estimate damaged building parts, which can accelerate rapid relief actions in earthquake-damaged areas. In addition, the proposed method promotes cost-effective relief actions because it filters out many intact buildings without omitting damaged buildings.