The Development and Uses of Crowdsourced Building Damage Information based on Remote-Sensing

Crowdsourced analysis of satellite and aerial imagery has emerged as a new mechanism to assess post-disaster impact in the past decade. Compared to standard ground-based damage assessments, crowdsourcing initiatives rapidly process extensive data over a large spatial extent and can inform many important emergency response and recovery decisions. We test three approaches to crowdsourcing post-earthquake building damage using 50cm resolution satellite imagery from the 2010 Haiti earthquake. Approach 1 further develops the predominant building-level map-based assessment method that has been implemented in earlier crowdsourcing initiatives. Two novel area-based assessment approaches were also developed, where users rate the level of build-ing damage in an image (Approach 2) and compare building damage between two images (Approach 3). The results of the two area-based approaches show a trend between crowdsourced and "true" field damage severity, which can be improved by weighting high-performing volunteers. Alternative methods, including Bayesian updating and network analysis, are proposed to analyze the paired comparison data from Approach 3. However, Approach 1 did not reach completion, because of the time intensive nature of building-level assessments.

Parallel to the crowdsourcing tests, an extensive ’demand survey’ of interviews with post-disaster practitioners was conducted to develop a timeline of six key decisions that are dependent on post-earthquake building damage data. The resulting framework can guide future research concerning rapid damage estimates to address decision-makers’ specific, and cross-cutting needs. Considering the results of the crowdsourcing tests and the demand survey, area-based approaches are promising methods to crowdsource building damage, because of its user-simplicity and ability to address specific post-disaster decisions.