A Method for Rapidly Determining the Seismic Performance of Buildings Based on Remote-Sensing Imagery and Its Application

Remote-sensing images are visually interpreted in this study to obtain information on buildings in the urban and rural areas of Ningxia, China. Overall, area estimates yielded by the proposed equations followed a normal distribution. Correlation and error analyses indicated that the coefficients are reasonable and reliable and that the building area estimates have an accuracy of 90% and are also reliable. These results were used in conjunction with drone aerial images, Baidu street view images, and paper maps to determine the seismic performance (SP) of the buildings in the study area. On this basis, the buildings were classified into three groups, namely, those with the required SP, suspected substandard SP, and substandard SP. Examination based on the field survey data collected from at least one sample site in each village and township in all 22 county-level divisions (CLDs) of Ningxia showed an average SP accuracy of 76% for all 22 CLDs and an SP accuracy exceeding 70% for 20 (91%) of the 22 CLDs. Based on this approach and the results obtained, the ArcGIS spatial analysis method was employed to determine the percentages and distribution patterns of the buildings in the three SP groups in the 22 CLDs. The results revealed the following features. Buildings with the required SP were clustered in the urban areas of each CLD, with a few in the village and township government seats. Buildings with suspected substandard SP were distributed predominantly in the rural-urban fringe (RUF) areas and the village and township government seats. Buildings with substandard SP were found primarily in urban villages, RUF areas, and urban areas. The soundness of the spatial analysis results was corroborated by the field survey data, lending credence to the feasibility of the proposed calculation method. This method can satisfy the real-world need for rapidly assessing the SP and distribution of buildings in a region before an earthquake occurs and provide a reliable reference for disaster prevention, mitigation, and relief efforts.