G-Dif: Geospatial Data Integration Framework to Rapidly Estimate Post-Earthquake Damage
With numerous technological and organizational advances, the amount of rapidly available information streams surrounding regional impacts after an earthquake has increased dramatically. Specifically, the post-disaster damage is either predicted with rapid loss models, estimated with remote sensing information, or measured through field surveys. SURI is developing a methodology to integrate these data sources to a single spatial distribution of a tangible damage metric which can inform response and recovery decisions.
This is part of a broader collaboration in which we are using the 2015 earthquake in Nepal as a case study to analyze post-disaster damages (disaster-induced losses) and needs (disaster-induced vulnerability).
This project is supported by the Global Partnership for Sustainable Development Data.
Kathmandu Living Labs (KLL)
Humanitarian OpenStreetMap Team (HOT)
World Bank Global Facility for Disaster Risk Reduction (GFDRR) and World Bank Big Data Program
NASA Jet Propulsion Lab and Advanced Rapid Imaging and Analysis Center (NASA-JPL/ARIA)
Loos, S., Lallemant, D., Baker, J. W., McCaughery, J., Yun, S.-H., Budhathoki, N., Khan, F., and Singh, R. (2020). “G-DIF: A geospatial data integration framework to rapidly estimate post-earthquake damage.” Earthquake Spectra, (in press).