Merging Remote Sensing and Socioeconomic Data to Improve Disaster Risk Assessment

Abstract:

Natural disasters disproportionately impact developing country economies while also impacting business operations for multi-national corporations that rely on supplies and manufacturing in affected areas. Understanding natural hazard risk is only a first step towards preparedness and mitigation—data on facilities, transportation, critical infrastructure, and populations that may be exposed to disasters is required to plan for events and properly assess risks. Detailed exposure data can be used in models to predict casualty rates, aggregate estimates of building damage or destruction, impacts on business operations, and the scale of recovery efforts required. These model outputs are useful for disaster preparedness planning by national and international organizations, as well as for corporations and the reinsurance industry seeking to better understand their risk exposure. Many of these data are lacking for developing countries.

Rapid assessment in areas with minimal data for disaster modeling is possible by combing remote sensing data, sample data on construction methods, facility and critical infrastructure data, and economic and demographic census information. This presentation focuses on the methods used to fuse the physical and socioeconomic data by presenting the results from two projects. The first project seeks to improve earthquake risk assessments in Asia using for the reinsurance industry, while the second project builds an integrated exposure database across five countries in Africa for use by international development organizations.