An entropy-based framework for efficient post-disaster assessment based on crowdsourced data

Abstract

After a disaster, authorities need to efficiently collect and analyze data from the disaster area in order to increase their situational awareness and make informed decisions. The conventional data acquisition methods such as dispatching inspection teams are often time-consuming. With the widespread availability of mobile devices, crowdsourcing has become an effective alternative means for data acquisition. However, the large amount of crowdsourced data is often overwhelming and requires triage on the collected data. In this paper, we introduce a framework to crowdsource post-disaster data and a new prioritization strategy based on the expected value of the information contained in the collected data (entropy) and their significance. We propose a multi-objective problem to analyze a portion of the collected data such that the entropy retrieved from the disaster area and the significance of analyzed data are maximized. We solve this problem using Pareto optimization that strikes a balance between both objectives. We evaluate our framework by applying it on bridges inspection after the 2001 Nisqually earthquake as a case study. We also investigate the feasibility of sending the crowdsourced data to the crowd for reviewing. The results demonstrate the effectiveness and feasibility of the proposed framework.