An uncertainty-aware framework for reliable disaster damage assessment via crowdsourcing

TLDR: This research improves the effectiveness of crowdsourcing in post-disaster damage assessment by enhancing the content and reliability of information gathered through public participation by presenting a novel framework for quantification and reduction of uncertainty in the outcome of participatory damage assessment.

Abstract: Accurate and timely estimation of incurred damages is a critical component of effective disaster management, usually performed by trained inspectors and experts. The limitations in resources and workforce can hinder the timely acquisition of critical information and make the process costly. Crowdsourcing and participatory disaster damage assessment have emerged as a possible solution to address this challenge. However, such approaches generally suffer from a lack of reliability. This research improves the effectiveness of crowdsourcing in post-disaster damage assessment by enhancing the content and reliability of information gathered through public participation. The paper presents a novel framework for quantification and reduction of uncertainty in the outcome of participatory damage assessment. First, to reduce the complexity and subjectivity, the classification of overall damage state is decomposed into more straightforward microtasks in the form of a questionnaire survey. A decision rule is implemented to infer the damage state of buildings from the participant responses. Second, an information-theoretic model based on a maximum a posteriori probability estimation is presented for obtaining an accurate probabilistic description of the inferred damage states while quantifying and accounting for the reliability of the citizen participants as well as the relative ambiguity of images. A pilot study is presented by involving 70 non-expert citizen participants to assess the post-disaster imagery of 60 buildings collected following Hurricane Harvey. A comparison of the outcome with the available expert labels shows relatively high accuracy. The proposed model also outperforms the common majority-vote approach, especially as the number of unreliable participants increases.