Crowd-sourced remote assessments of regional-scale post-disaster damage

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, but utilization has been limited due to uncertainty in the results. New methods for crowdsourced satelliteimagery- based damage assessment were assessed through three test approaches. Approach 1 further develops the predominant building-by-building map-based assessment method. Two novel area-based assessment approaches were implemented, where users rate the level of building damage in an image in Approach 2 and compare building damage between two images in Approach 3. Preliminary results from statistical aggregation and regression models indicate that crowdsourced volunteers can visually identify building damage in images on an aggregated, as opposed to building-by-building basis. The correlation between crowd-identified and true damage can be further improved by weighting responses based on user and image characteristics. Results show promise in a novel method of crowdsourcing damage using area-based assessments, which addresses decision-makers' need for aggregated post-disaster loss estimates in a rapid timeframe.