Validation of CyborgCrowd Implementation Possibility for Situation Awareness in Urgent Disaster Response -Case Study of International Disaster Response in 2019-

At disaster response, it is essential to grab whole picture of damage situation quickly and early after disaster occurrence in order to make disaster response effective and efficient. However, it takes much time to understand damage situation because there is not enough information about it. Against this issue, we proposed implementation of CyborgCrowd for situation awareness in disaster response. In order to validate its possibility, we planned the first international disaster drill in October, 2019. In this drill, we simulated to detect flooded area by West Japan Flood occurred in 2018 from aerial photos by collaboration between crowdsourcing and AIs following Human-in-the-Loop process. Especially, in this drill, AIs were also crowdsourced. In this research, we validated the transition of the efforts from crowdsourcing and AIs to detecting flooded area, and verified the accuracy of result by comparing with the actual flooded area published by Geospatial Information Authority of Japan. Furthermore, we found some suggestion about features of detection results by humans and AIs. For example, some humans detected flooded area roughly, however AIs detected it much closely. Based on those features, we proposed the way to decrease the difference between results by humans and AIs. This was essential for local responders to understand the whole picture of damage situation after disaster occurrence urgently. In this paper, we introduced the framework of international disaster drill, clarified the result of validation, and mentioned the possibility of effective collaboration between crowdsourcing and AIs for quick situation awareness in disaster response.