Crowdsourcing Post-Earthquake Building Damage
From power lines to homes, an earthquake has the power to devastate entire regions. Damage to the housing sector alone can account for almost 60% of the total losses from an earthquake. Affected countries typically have one chance to request aid from international donors during a conference held just weeks after the earthquake. Therefore, the government of the affected country must have a reliable estimate of the extent of housing damage to be able to quantify the amount of aid needed. This information is presented in a report called the Post-Disaster Needs Assessment (PDNA).
Trained engineers can perform on-the-ground assessments of building damage. While such assessments are very reliable, this process may take several months – far too long to be included in the PDNA. It’s for this reason that the Stanford Urban Resilience Initiative (SURI) is researching ways to rapidly assess building damage using satellite imagery and the power of the crowd. This method has been used before, but its reliability has been questioned. In partnership with Humanitarian OpenStreetMap Team (HOT OSM), Heidelberg University, the World Bank’s Global Facility for Disaster Reduction and Recovery (GFDRR), and University of Colorado Boulder, SURI launched three surveys to see how crowdsourced damage assessments can be improved for use after future disasters.
Stanford Urban Resilience Initiative: Gitanjali Bhattacharjee, Sabine Loos, Karen Barns, David Lallemant, Greg Deierlein, Anne Kiremidjian, Jack Baker
University of Colorado Boulder: Robert Soden
GIScience Research Group at Heidelberg University: Benjamin Herfort, Melanie Eckle
HOT: Cristiano Giovando, Blake Girardot
World Bank GFDRR: Keiko Saito
This material is based upon work supported by the National Science Foundation under Grant No. 1645335/EAGER “A dynamic, reliability-weighted, multi-pass probabilistic framework to reduce uncertainty in crowd-sourced post-disaster damage assessments”. Satellite imagery is kindly provided by DigitalGlobe through the Open Data Program.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.