SafeWitness: Crowdsensing-Based Geofencing Approach for Dynamic Disaster Risk Detection

As the frequency of disasters increases worldwide, it has become increasingly important to raise awareness of the risks and mitigate their effects through effective disaster management. Anticipating disaster risks and ensuring timely evacuations are crucial. This paper proposes SafeWitness, which dynamically captures the evolving characteristics of disasters by integrating crowdsensing and GIS-based geofencing. It not only enables real-time disaster awareness and evacuation support but also provides spatial context awareness by mapping the disaster area based on GIS road information and temporal context awareness by using crowdsensing to track the progress of the disaster. This approach increases the effectiveness of disaster management by providing explicit, data-driven insights for timely decision making and risk mitigation. The experimental results reveal that the proposed method improved the F1-scores in the hazard and warning zones compared to the domain-based approach. The result increased by 12% in the hazard zone and by 55% in the warning zone compared to the traditional technique. Through user sampling, we enhanced the SafeWitness F1-score in the hazard zone by 6 times and in the warning zone by 2.8 times compared to the method without user sampling. In conclusion, SafeWitness offers a more precise perception of disaster areas than traditional domain-based area definitions, and the experimental results demonstrate the effectiveness of user sampling. Decision-makers and disaster management professionals can use the proposed method in urban disaster scenarios.