Empowering Crisis Response Efforts: A Novel Approach to Geolocating Social Media Images for Enhanced Situational Awareness

Abstract

Social media and online platforms play an important role in assessing the impact of a natural disaster, especially in the immediate aftermath of the event. The information from social media demonstrated the potential to accelerate the response to a crisis. Locating social media images is critically important to help the victims immediately,  mobilize community support, and provide enhanced situational awareness. However, finding the location of relevant social media images still challenges humans and computational processes.

This study introduces a social media image classifier aimed at enhancing crowdsourced geolocation. The model is trained using data annotated by experts in disaster risk management. Its main goal is to reduce the difficulty of geolocating images by detecting those that are ``easy'' to geolocate. We combined the classifier with an ad-hoc crowdsourcing platform and tested it with pictures posted during a crisis. The experimental results indicate that the proposed approach speeds up the geolocation process of social media images while increasing the level of location precision