Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System
This work addresses the need for better crisis management data for emergency responders, though it appears incremental as it builds on existing sensing and AI methods.
The paper tackled the problem of limited information from online social networks during disasters by developing HERMES, a system that uses hybrid sensing and AI to enrich data, resulting in up to 7x increased density, 18x increased variety, and 30% improved geographic coverage of retrieved information.
People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid sensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to 7x) and the variety (up to 18x) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity.