Supporting geospatial climate hazard reporting using computer vision and text generation of social media imagery

Citizen reports and social media images that capture the aftermath of natural disasters contain important information for emergency responders. Currently, these data sources are not fully integrated into existing systems or require labour-intensive user input, which can be challenging in critical situations. In this paper, we apply computer vision services to publicly available imagery to derive meaningful information, extract objects and create text descriptions. This research builds on our previous work and enhances available hazard maps with (near) real-time weather and traffic information. Through this geospatial-based workflow, we aim to reduce climate hazard reporting friction and support operational response to incidents.