Identifying Collapsed Buildings after Sarpol-e Zahab Earthquake Using Multisensor Analysis and Machine Learning

Earthquakes and their consequences should be studied in detail in order to reduce the number of casualties in future events. From the beginning of the twenty first century until now more than 800000 deaths were reported, in which most of the casualties are located in Alp-Himalayan seismic belt. Bam earthquake in 2003 in central Iran, with more than 26000 casualties, Indian Ocean earthquake in 2004, with approximately 200000 casualties, Sichuan earthquake in 2008 in China with more than 96000 casualties, and Haiti earthquake in 2010 in Haiti with approximately 321000 casualties are only a few given examples that how devastating the earthquakes can be. Instant deaths right after a strong earthquake is primarily because of physical contact of rubbles material with exposed people, but the second phase of casualties emerge due to injuries, suffocation of trapped people among the rubbles and wasted materials, and collateral hazards such as fire. Although the instant deaths look inevitable, second phase casualties can be decreased by addressing rapid disaster response based on recent remote sensing earth observation systems to bring the quality of search and rescue teams to an actionable level, especially for night-time earthquakes. In SAR remote sensing imagery, addressing of seismic damage states initiated with simple indices such as difference and correlation of SAR backscatters of pre- and post-event images, difference of coherence value of interferometric phase analysis, and their combination. Furthermore, regression analysis of SAR backscattering of pre- and post-event images together with seismic intensity were also applied for deeper understanding of the earthquake damages. In the recent developments of earthquake damage assessment, combination of multitemporal dual-polarized SAR data, combination of multitemporal ascending-descending SAR data and only post-event SAR data are common methods to decrease the level of uncertainty. In the optical remote sensing, damage assessment was initiated by visual comparison of pre- and post-event images. However it is possible to apply methodologies based on only post-event images if lower accuracy is needed. Therefore, visual interpretation of optical images, rather than automated change detection, is widely used in practice for building damage detection. Saito et al. (2004) visually interpreted collapsed buildings using three IKONOS images taken before and after the Gujarat earthquake, and confirmed the quality of the results by ground survey data. Further, Saito and Spence (2005) compared the visual interpretation results from only post-event QuickBird images with those from pre- and post-event images, and revealed that the building damage tended to be underestimated when only post-event images were available. Adams et al. (2005) used a visualization system integrated pre- and post-event QuickBird imagery to direct rescuers to the hardest hit areas and support efficient route planning and progress monitoring in the emergency response phase of the Bam earthquake. By comparing the pre- and post-event QuickBird imagery visually, Yamazaki et al. (2005) classified the damaged buildings caused by the Bam earthquake into four damage grades (EMS98). Comparing the results to field survey data revealed that the pre-event imagery was more helpful in detecting lower damage grades through visual interpretation. Here various machine learning based techniques for performance understanding of the classifiers in an urban scale is presented. This study covers a comprehensive seismic damage assessment of Sarpol-e Zahab town in western Iran which was affected by an earthquake M 7.3 on 12 November, 2017. The damage concept is evaluated using both synthetic aperture radar (SAR) and optical images. Two pre-event and one post-event dual-polarized high resolution SAR images of ALOS-2 satellite, and one pre-event and one post-event very high resolution optical images of WorldView-2 satellite (4 bands) are contributed in the comprehensive seismic damage assessment. In SAR dataset, twenty-four influential parameters are extracted from interferometric phase correlation (differential coherence), differential intensity, and differential texture analysis of HH and HV channels, whereas in optical dataset, twenty influential parameters are derived from differential texture analysis of red, green, blue and infrared (IR) bands. For the derived parameters of each dataset, principal component analysis (PCA) and machine learning based algorithms (i.e. random forests, support vector machine, naive Bayes, k-nearest neighbors and regression tree) are carried out in order to extract the damage maps and their related accuracy with respect to the calibration data which is acquired from United Nations Institute for Training and Research (UNITAR).