Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System

TL;DR

HERMES is a hybrid sensing system that enhances social media data collection during emergencies, significantly improving the quantity, geographic coverage, and detail of damage information through AI techniques.

Contribution

The paper introduces HERMES, a novel hybrid sensing approach that combines social media data with AI to improve emergency data collection and geographic information retrieval.

Findings

Increased damage information availability

Up to 7x higher data density

Up to 30% improvement in geographic coverage

Abstract

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.