Assessing Climate Disaster Vulnerability in Peru and Colombia Using Street View Imagery: A Pilot Study - Chaofeng Wang; Sarah E. Antos; Jessica G. Gosling-Goldsmith; Luis M. Triveno; Chunwu Zhu; Jason von Meding; Xinyue Ye (Buildings, 2023)
Community and household vulnerability to natural hazards, e.g., earthquakes, hurricanes, and floods, is a concern that transcends geographic and economic boundaries. Despite the abundance of research in this field, most existing methods remain inefficient and face the challenge of data scarcity. By formulating and investigating the correlation between the household vulnerability and street view images of buildings, this research seeks to bridge the knowledge gap to enable an efficient assessment. Especially in developing countries, the widespread prevalence of outdated or inadequately enforced building codes poses a significant challenge. Consequently, a considerable portion of the housing stock in these regions fails to meet acceptable standards, rendering it highly vulnerable to natural hazards and climate-related events. Evaluating housing quality is crucial for informing public policies and private investments. However, current assessment methods are often time-consuming and costly. To address this issue, we propose the development of a rapid and reliable evaluation framework that is also cost-efficient. The framework employs a low-cost street view imagery procedure combined with deep learning to automatically extract building information to assist in identifying housing characteristics. We then test its potential for scalability and higher-level reliability. More importantly, we aim to quantify household vulnerability based on street view imagery. Household vulnerability is typically assessed through traditional means like surveys or census data; however, these sources can be costly and may not reflect the most current information. We have developed an index that effectively captures the most detailed data available at both the housing unit and household level. This index serves as a comprehensive representation, enabling us to evaluate the feasibility of utilizing our model’s predictions to estimate vulnerability conditions in specific areas while optimizing costs. Through latent class clustering and ANOVA analysis, we have discovered a strong correlation between the predictions derived from the images and the household vulnerability index. This correlation will potentially enable large-scale, cost-effective evaluation of household vulnerability using only street view images.