In the field of remote sensing disaster assessment, crowdsourcing assembles the intelligence of the network users to complete the disaster assessment tasks. However, there is a problem that the quality of the workers' answers is not reliable in the real crowdsourcing platform. In this study, a new model combined with quality control for crowdsourcing assessment of collapsed buildings after the earthquake is proposed. A case study of Yushu earthquake was presented and the experimental platform was set up to collect crowdsourcing disaster assessment data. First, the gold data and consistency test method were used to filter out low-quality crowdsourcing data. Then, the reliable disaster interpretation results were derived by the crowdsourcing assessment model. The experimental results show that the assessment accuracies of the collapsed buildings in the test area were 70.59% without the quality control and 85.29% with the quality control, which indicates that the proposed model effectively increases the accuracy of the crowdsourcing disaster interpretation. Therefore, crowdsourcing provides reliable visual interpretation results based on the proposed method, which is of importance for remote sensing disaster assessment.
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