FLOOD SIMULATION: INTEGRATING UAS IMAGERY AND AI-GENERATED DATA WITH DIFFUSION MODEL

The primary goal of early disaster impact assessments is to gather georeferenced data about affected areas. Floods, a major natural calamity, pose challenges in data collection and response coordination. The use of Unmanned Aerial Systems (UAS) has significantly improved data acquisition in flood-impacted regions, offering a cost-effective method for obtaining high-quality images. However, issues like accurate image description and data scarcity remain. Addressing these, we propose two novel methods: GPT models for precise image-to-text conversion and diffusion models for flood data simulation. GPT models enhance data quality with accurate descriptions, while diffusion models extend our dataset by simulating diverse flood scenarios, enriching our training resources. Further analysis of this augmented dataset for similarity demonstrates a wide-ranging variety in the data, highlighting a diverse array of elements within images.